Remote control method and system for new energy automobile charging pile

Abstract

The invention provides a remote control method and system for a new energy vehicle charging pile, and the method comprises the steps: collecting a vehicle battery state, a user demand, and the operation information of a power grid and the charging pile, and integrating the information into a standardized comprehensive state variable; dynamically adjusting the initial charging power of the single user according to the health state of the vehicle battery to obtain adjusted charging power; calculating the charging priority of the user by combining the distributed power with a dynamic weight adjustment model based on time decay; receiving a multi-user distribution strategy, and constructing a real-time adjustment variable set in combination with the operation information of the charging pile; and the power grid load change is monitored in real time. According to the invention, on one hand, a flexible solution is provided for personalized demands of users, and on the other hand, efficient utilization and active management of system resources are realized through energy sharing and remote dynamic control, so that main defects in the prior art are overcome, and the system has important technical value and application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of new energy vehicle charging piles, and in particular relates to a new energy vehicle charging pile fault diagnosis method and system. Background Art

[0002] With the rapid popularization of new energy vehicles, charging piles, as one of the core infrastructures, are facing increasingly complex usage requirements and technical challenges. At present, charging piles not only need to meet basic charging functions, but also need to achieve efficient coordination in terms of grid load pressure, vehicle battery health management, and multi-user concurrent needs. However, existing technologies have obvious defects in the following key aspects: Single charging mode, difficult to meet diverse needs: Most existing charging piles provide fixed charging power and mode, and cannot be dynamically adjusted according to the specific needs of different users. For example, for users who need to recharge quickly, the charging efficiency may not be high enough; and for users who focus on battery life maintenance, there is a lack of adaptive charging strategy based on the battery health state (State of Health, SOH), which will accelerate the degradation and loss of the battery. In addition, existing charging piles lack scene adaptation capabilities. For example, in commercial parking lots or multi-user sharing scenarios, charging resources cannot be reasonably allocated, which easily leads to some vehicles occupying resources for a long time, while other vehicles cannot be charged in time.

[0003] Low energy efficiency and failure to fully utilize bidirectional power flow: Although bidirectional charging technology (Vehicle-to-Grid, V2G) has been gradually introduced, most existing charging piles still only support one-way power flow and cannot achieve bidirectional energy interaction between vehicles and the grid. Under peak grid load conditions, new energy vehicles cannot be effectively used as distributed energy storage units to alleviate grid pressure, and during off-peak hours, charging power cannot be flexibly adjusted to reduce electricity costs or achieve efficient energy utilization.

[0004] Insufficient remote management and control functions: Existing remote control systems mainly focus on basic start and stop functions and lack in-depth real-time optimization capabilities. For example, in multi-user scenarios, it is impossible to effectively identify the urgency and demand priorities of different users, thereby realizing intelligent allocation of resources. At the same time, for abnormalities that may occur during charging (such as equipment overheating and current fluctuations), the fault handling of existing technologies is mostly passive response, lacking active prevention and real-time intervention capabilities. This defect results in the inability to fully guarantee both user experience and system stability.

[0005] In summary, the existing technology cannot achieve efficient and intelligent coordination between user needs, energy utilization and system management. These defects restrict the development of new energy vehicle charging piles, and a smarter and more flexible remote control method and system are urgently needed. Summary of the invention

[0006] The purpose of the present invention is to propose a new energy vehicle charging pile fault diagnosis method and system, which, on the one hand, provides a flexible solution for the personalized needs of users, and on the other hand, realizes efficient utilization and active management of system resources through energy sharing and remote dynamic control, thereby overcoming the main defects of the prior art and having important technical value and application prospects.

[0007] In order to achieve the above object, a first aspect of the present invention provides a remote control method for a new energy vehicle charging pile, the method comprising the following steps: S1. Collect vehicle battery status, user needs, and operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables; Wherein, the vehicle battery status includes the vehicle battery health status, current battery percentage and battery temperature; The user requirements include the target power level that the user expects to be charged, the charging completion time that the user expects, and the maximum charging power that the user can accept; The operation information of the charging pile includes the total power of the current charging pile and the grid load rate; S2. Based on the comprehensive state variables, with the user's expected target power and the user's expected charging completion time as constraints, calculate the initial charging power of a single user, and then dynamically adjust the initial charging power of the single user according to the vehicle battery health status to obtain the adjusted charging power, and further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate the single user charging strategy and the single user charging strategy set in combination with the user demand and the healthy adjusted power; S3. Analyze the available allocated power in the multi-user scenario according to the dynamic load of the current power grid, calculate the user charging priority according to the allocated power combined with the dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculate the actual allocated power of each user according to the user's charging priority and the user's required power using a normalized weight allocation strategy, and verify the actual allocated power of each user, and integrate the actual allocated power allocation of each qualified user into a multi-user allocation strategy; wherein the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time and the user's final adjusted priority; S4. Receive the multi-user allocation strategy, and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, calculate its actual energy demand, and calculate the user's charging time in combination with the user's actual charging power. In combination with the operation information of the charging pile and the user's actual charging power, dynamically adjust the real-time power of each user, introduce the V2G mechanism to update the real-time adjustment variable set, and reflect the grid load changes after the feedback execution adjustment variable set. The V2G mechanism is specifically: When the grid load exceeds the warning threshold, the V2G mode of the user's vehicle is triggered, and the actual feedback power Calculated according to the following formula: ; in, The safety time buffer required for feedback (in hours) to ensure the minimum battery level of the vehicle; is the current battery power percentage of user i, is the current remaining power, For users Total battery capacity; For users The power fed back to the grid by the vehicle depends on the current charge and minimum requirements; The actual allocated power for user i; Update real-time adjustment variable collection , reflecting the change of grid load after feedback: ; in, is the updated grid load factor, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging pile.

[0008] S5. Monitor the changes in grid load in real time. If any abnormality is detected, adjust the real-time power allocation of each user immediately and output the real-time monitoring results for feedback, including the dynamic changes of all key state variables, and integrate the real-time power allocation, energy feedback power and grid load of all users into the final adjustment state.

[0009] Furthermore, S1 also includes cleaning the collected comprehensive state variables, eliminating abnormal values, and filling in missing values ​​through interpolation to ensure the integrity of the state variables, taking battery data as the main time axis, and aligning the power grid and user data through interpolation and matching methods.

[0010] Furthermore, the calculation of the initial charging power of a single user , calculated as follows: ; in, The maximum charging power allowed by the user; is the current remaining power, is the total capacity of the battery; The user's expected completion time; For charging efficiency, The target power level that the user expects to charge; The initial charging power of a single user is dynamically adjusted according to the vehicle battery health status to obtain an adjusted charging power, It is expressed as: ; in, is the health status factor, through controlling Dynamically limit power, To adjust the parameters; Ensure that the minimum charging power is 50% of the normal value to avoid excessive derating that may cause a decrease in user experience; Finally, the single-user charging strategy is generated by combining user demand and health-adjusted power. : ; in, The final charging power is adjusted based on time requirements, health status and temperature factors; The completion time entered by the user is directly used for scheduling priority; is the user priority, initialized to , Small positive number to avoid zero value.

[0011] Furthermore, the final charging power is constrained: If the battery temperature Approaching the critical threshold, further To perform dynamic derating: ; in, is the adjusted charging power, is the safe operating temperature threshold; is the critical temperature threshold; is the temperature adjustment coefficient.

[0012] Furthermore, the single-user charging strategy Verify the following: Ensure the final charging power , The maximum charging power acceptable to the user should be avoided to avoid exceeding the power allowed by the user; Ensure the final charging power , The total available power of the charging pile should be avoided to exceed the single-port power limit of the charging pile.

[0013] Furthermore, the available allocated power in a multi-user scenario is analyzed according to the dynamic load condition of the current power grid. , calculated as follows: ; in, is a nonlinear adjustment function, and is an empirical parameter that controls the impact of load on the distributed power; The dynamic weight adjustment model is expressed as: ; in, is the priority of user i, For users The cumulative charging time used; The user's goal completion time; To prevent small positive numbers with zero denominator; The actual allocated power of each user is calculated by using a normalized weight allocation strategy based on the user's charging priority and the user's required power. , expressed as: ; in, It is a sparse parameter used to prevent some users from occupying high-power resources for a long time; To normalize the square weight term, enhance the sparsity of high-weight users.

[0014] Furthermore, the actual allocated power of each user is verified. Do the following constraints apply? A. , ensuring that the total power does not exceed the allocated upper limit; B. For each user, , to avoid exceeding the user's hardware limitations; Then, the actual power allocation of each user is integrated into the multi-user allocation strategy : ,in, is the final adjusted dynamic priority.

[0015] Further, the S4 includes, for each user , calculate its actual energy demand , and combined with the actual allocated power of each user Calculate charging time: ; in, For users Target power consumption (unit: kWh); is the current remaining power, For users Total battery capacity; Combined with the grid status The actual power allocated to the user , dynamically adjust the real-time power of each user , introducing the grid load smoothing term : ; in, ,in is the smoothing coefficient, which is used to control the intensity of load smoothing.

[0016] Further, the S5 comprises: Monitor changes in key variables during operation, including: updated grid load rate, real-time power allocation for each user, battery temperature and current power ; If an abnormality is detected, or , instant adjustment : ; in, is the safety temperature threshold, is the critical temperature threshold.

[0017] In a second aspect of the present invention, a remote control system for a new energy vehicle charging pile is provided, the system comprising: The state variable acquisition unit is used to collect the vehicle battery status, user demand, and the operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables; Wherein, the vehicle battery status includes the vehicle battery health status, current battery percentage and battery temperature; The user requirements include the target power level that the user expects to be charged, the charging completion time that the user expects, and the maximum charging power that the user can accept; The operation information of the charging pile includes the total power of the current charging pile and the grid load rate; A single-user charging strategy generating unit is used to calculate the initial charging power of a single user based on the comprehensive state variables, with the target power expected to be charged by the user and the charging completion time expected by the user as constraints, and then dynamically adjust the initial charging power of the single user according to the health status of the vehicle battery to obtain the adjusted charging power, further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate the single-user charging strategy and the single-user charging strategy set in combination with the user demand and the healthy adjusted power; A multi-user charging strategy generation unit is used to analyze the available allocated power in a multi-user scenario according to the dynamic load situation of the current power grid, calculate the user charging priority according to the allocated power combined with a dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculate the actual allocated power of each user according to the user's charging priority combined with the user's required power using a normalized weight allocation strategy, and verify the actual allocated power of each user, and integrate the actual allocated power allocation of each qualified user into a multi-user allocation strategy; wherein the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time and the user's final adjusted priority; The V2G bidirectional feedback control unit is used to receive the multi-user allocation strategy and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, its actual energy demand is calculated, and the user's charging time is calculated in combination with the user's actual charging power. In addition, the real-time power of each user is dynamically adjusted in combination with the operation information of the charging pile and the user's actual charging power. The V2G mechanism is introduced to update the real-time adjustment variable set to reflect the change in the grid load after the feedback execution of the adjustment variable set. The V2G mechanism is specifically: When the grid load exceeds the warning threshold, the V2G mode of the user's vehicle is triggered, and the actual feedback power Calculated according to the following formula: ; in, The safety time buffer required for feedback (in hours) to ensure the minimum battery level of the vehicle; is the current battery power percentage of user i, is the current remaining power, For users Total battery capacity; For users The power fed back to the grid by the vehicle depends on the current charge and minimum requirements; The actual allocated power for user i; Update real-time adjustment variable collection , reflecting the change of grid load after feedback: ; in, is the updated grid load factor, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging pile.

[0018] The real-time monitoring control unit is used to monitor the changes in the grid load in real time. If an abnormality is detected, the real-time power allocation of each user will be adjusted immediately, and the real-time monitoring results will be output for feedback, including the dynamic changes of all key state variables, integrating the real-time power allocation, energy feedback power and grid load of all users into the final adjustment state.

[0019] The beneficial technical effects of the present invention are at least as follows: The present invention collects the health status (SOH) and charge status (SOC) of the vehicle battery in real time, and dynamically adjusts the charging power output in combination with user needs and battery loss models. This strategy can provide a "health priority" mode or a "fast charging" mode according to the needs of different users, extend battery life and optimize charging efficiency, and fundamentally solve the problem of a single charging mode in the prior art.

[0020] V2G and G2V bidirectional charging technologies are introduced to control the energy flow between vehicles and the power grid through a remote platform. During peak load of the power grid, vehicles are allowed to feed excess power back to the power grid; during off-peak hours, charging power is automatically adjusted to achieve low-cost charging. In addition, by optimizing the algorithm to allocate charging resources in multi-user sharing scenarios, the overall energy utilization efficiency of charging piles is improved, solving the problems of one-way energy flow and uneven resource allocation in existing technologies.

[0021] A set of automatic switching mechanisms for charging modes based on scenario perception has been designed. The remote management platform can automatically identify charging scenarios and select the best mode based on vehicle status, user reservation information, and grid load conditions. For example, the "balanced mode" can be enabled in commercial parking lots to optimize resource allocation for multiple users; the "energy-saving mode" is provided for personal home use to extend battery life. In addition, through the anomaly detection and fault warning functions, the remote management platform can proactively identify potential problems and take intervention measures, significantly improving the safety and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0023] Figure 1 The present invention is a flow chart of a remote control method for a new energy vehicle charging pile. DETAILED DESCRIPTION

[0024] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0025] like Figure 1 As shown, an embodiment of the present invention provides a remote control method for a new energy vehicle charging pile, the method comprising: S1. Collect vehicle battery status, user needs, and operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables.

[0026] Specifically, the vehicle's charging communication protocol (such as ISO15118) is used to obtain the vehicle's battery status data in real time from the charging interface: : StateofHealth, battery health status, indicating the ratio of battery capacity to a new battery (range: 0-1).

[0027] : StateofCharge, the current battery power percentage (range: 0-100).

[0028] : Battery temperature (unit: ℃).

[0029] Time synchronization is performed on the collected data to ensure data consistency, such as serializing the data stream using timestamps.

[0030] Among them, user demand information collection and structuring: The user enters the following information through the remote interface: : The target amount of electricity that the user expects to charge (unit: kWh).

[0031] : The user's expected charging completion time (unit: minutes).

[0032] : The maximum charging power acceptable to the user (unit: kW).

[0033] Store this information as a user demand vector: .

[0034] Initialize user priority flags for multi-user scheduling:

[0035] Furthermore, the charging pile status is synchronized with the grid information: The charging pile collects and uploads the following information in real time: : The total available power of the charging pile (unit: kW).

[0036] : Grid load condition, indicating the load rate of the current line (range: 0-1).

[0037] Furthermore, this information is integrated into grid state variables: ; Furthermore, the data of vehicles, power grids and users are integrated into a unified set of state variables: ; : The health status of the vehicle battery.

[0038] :User demand information.

[0039] : The operating status of the power grid and charging piles.

[0040] Furthermore, data consistency check and cleaning: For the collection Clean and remove outliers.

[0041] Interpolation is used to fill in missing values ​​to ensure that the state variables are complete. For example: ; Data synchronization processing: Using battery data as the main time axis, grid and user data are aligned through interpolation and matching methods.

[0042] Furthermore, the output variables are: Organize into standardized input for subsequent steps: ; The definition of each sub-variable is consistent with the subsequent steps and serves as input for subsequent strategy generation and optimization.

[0043] S2. Based on the comprehensive state variables, with the user's expected target power and the user's expected charging completion time as constraints, calculate the initial charging power of a single user, and then dynamically adjust the initial charging power of the single user according to the vehicle battery health status to obtain the adjusted charging power, and further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate a single-user charging strategy and a single-user charging strategy set in combination with user needs and the healthy adjusted power.

[0044] Specifically, receive the comprehensive state variable output from step 1 , clarify its components: : Vehicle battery health status ( ), current battery percentage ( ), battery temperature ( ); : User's target power consumption ( ), expected charging completion time ( ), maximum receiving power ( ); : The total power of the current charging pile ( ) and grid load factor ( ).

[0045] Through the above input, a basic data environment is provided for each user, and the data is ensured to remain consistent in subsequent steps.

[0046] Furthermore, the initial charging power of a single user is calculated: Considering user needs and the current state of the battery, an initial power calculation formula based on time priority is designed: ; : The maximum charging power allowed by the user (unit: kW); : Current remaining power, is the total battery capacity (unit: kWh); : User expected completion time (unit: hours); : Charging efficiency (0.9-1.0, dynamically obtained or set to a fixed value).

[0047] In this formula, the user's target power and expected completion time are directly used as constraints to ensure that the calculated power meets the time priority requirements and is operational.

[0048] Furthermore, adjust the power to protect the battery health: To protect battery life, Dynamically adjust initial power , and the adjusted charging power is obtained : ; : Health status factor, through control ( To adjust the parameters, usually set to 0.8-1.2) Dynamically limit power; : Ensure that the minimum charging power is 50% of the normal value to avoid excessive derating that may reduce user experience.

[0049] In this formula, an innovative Nonlinear adjustment term, through optimization The value achieves a balance in battery power protection for different health states.

[0050] Furthermore, a health protection mechanism with temperature constraints is introduced: If the battery temperature Approaching a critical threshold (e.g. ), and further To perform dynamic derating:

[0051] : Safe operating temperature threshold (e.g. ); : Critical temperature threshold (e.g. ); : Temperature adjustment coefficient (usually set to 0.1-0.5).

[0052] This formula minimizes the threat of temperature changes to battery life by making a secondary adjustment to power based on temperature.

[0053] Finally, the single-user charging strategy is generated by combining user demand and health-adjusted power: ; : Final charging power, adjusted based on time requirements, health status and temperature factors; : The completion time entered by the user is directly used for scheduling priority; : User priority, initialized to ( to avoid small positive numbers with zero values).

[0054] Further verification and output: For the generated Verify the following: make sure , avoid exceeding the power allowed by the user; make sure , to avoid exceeding the single-port power limit of the charging pile.

[0055] Output a complete charging strategy for a single user , including power and priority to satisfy multiple constraints.

[0056] S3. Analyze the available allocated power in a multi-user scenario based on the dynamic load conditions of the current power grid, calculate the user charging priority based on the allocated power combined with a dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculate the actual allocated power for each user based on the user's charging priority and the user's required power using a normalized weight allocation strategy, and verify the actual allocated power for each user, and integrate the actual allocated power allocation for each qualified user into a multi-user allocation strategy; wherein, the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time, and the user's final adjusted priority.

[0057] Specifically, receive the single-user policy set generated in step 2 , where each user policy is: ;

[0058] :user The final adjusted charging power (in kW), adjusted for time constraints and health protection; :user The target completion time (in minutes) indicates the urgency of the user's needs; :user The priority is calculated in step 2 based on the completion time and requirement importance.

[0059] Receive the charging pile status and grid information from step S1 ; : The total available power of the charging pile (in kW), providing a hard limit for allocation; : Grid load rate (range: 0-1). When the load is high, the distributed power needs to be reduced to prevent the system from overloading.

[0060] Furthermore, all user policies and system states are integrated into a globally allocated state variable , used to unify the context of describing resource allocation:

[0061] Furthermore, the global allocated power cap is calculated: In a multi-user scenario, according to Dynamic calculation of available allocated power , introducing innovative grid load regulation factors : ; : nonlinear adjustment function, and is an empirical parameter that controls the impact of load on the distributed power; Example: When (Grid load 50%), (Assumption ),but The formula introduces nonlinear reduction when the grid load is high, effectively alleviating peak pressure.

[0062] Furthermore, based on the traditional priority, a time-decayed dynamic weight adjustment model is designed. , introducing the "impact of cumulative charging time on priority": ; :user The cumulative charging time used; : The user's target completion time; : Prevent small positive numbers with a denominator of zero.

[0063] When the user As the cumulative charging time increases, its priority It will be gradually reduced to ensure that resources are tilted towards users with more urgent needs.

[0064] Furthermore, according to the user's dynamic weight and required power , the normalized weight allocation strategy is used to calculate the actual allocated power of each user , while introducing an innovative sparse regularization term : ; : Sparseness parameter, used to prevent some users from occupying high power resources for a long time; : Normalize the square weight term to enhance the sparsity of high-weight users.

[0065] This formula ensures that power allocation not only considers demand priority but also balances allocation fairness through a sparse regularization term.

[0066] Furthermore, the verification and allocation strategy outputs: Verify the actual power distribution generated Do the following constraints apply? , ensuring that the total power does not exceed the allocated upper limit; For each user, , to avoid exceeding user hardware limitations.

[0067] The actual power allocation of each user is combined into a multi-user allocation strategy: , : The final adjusted dynamic priority for subsequent updates and feedback. Output , providing input for the next step of real-time energy regulation.

[0068] Furthermore, the distribution results are recorded and The comparison results are used as real-time feedback indicators of system load and user satisfaction; by dynamically adjusting parameters , optimizing the flexibility and robustness of the overall allocation model.

[0069] S4. Receive the multi-user allocation strategy, and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, calculate its actual energy demand, and calculate the user's charging time in combination with the user's actual charging power. Combined with the operation information of the charging pile and the user's actual charging power, dynamically adjust the real-time power of each user, introduce the V2G mechanism to update the real-time adjustment variable set, and reflect the changes in the grid load after the feedback execution of the adjustment variable set.

[0070] Specifically, enter the multi-user allocation strategy set in step 3 ,in: ; : Assigned to user Actual charging power (unit: kW); :user target completion time; :user Final adjusted priorities.

[0071] Receive the system state variables from step S1 : : Total power of charging pile (unit: kW); : Grid load rate (range: 0-1).

[0072] Integrate the above inputs into a set of real-time adjustment variables , providing a unified data environment for energy regulation and state feedback: ; Furthermore, for each user , calculate its actual energy demand (Unit: kWh), and combined with the allocated power Calculate charging time: ; :user Target power consumption (unit: kWh); : Current remaining power, For users The total battery capacity.

[0073] Combined with the grid status The actual power allocated to the user , dynamically adjust the real-time power of each user , introducing an innovative grid load smoothing term : ; ,in is the smoothing coefficient, which is used to control the intensity of load smoothing.

[0074] This formula ensures that power output is gradually reduced under high load conditions to ensure grid stability.

[0075] Furthermore, the energy feedback (V2G) mechanism is introduced: When the grid load Exceeding the warning threshold (e.g. 0.8), triggering the energy feedback (V2G) mode of the user's vehicle. Actual feedback power Calculated according to the following formula: ; : The safety time buffer required for feedback (unit: hours) to ensure the minimum power level of the vehicle; :user The power that a vehicle feeds back to the grid depends on the current charge and minimum requirements.

[0076] Update real-time adjustment variables , reflecting the change of grid load after feedback: ; S5. Monitor the changes in grid load in real time. If any abnormality is detected, adjust the real-time power allocation of each user immediately and output the real-time monitoring results for feedback, including the dynamic changes of all key state variables, and integrate the real-time power allocation, energy feedback power and grid load of all users into the final adjustment state.

[0077] Specifically, the changes in key variables during the operation of the monitoring system include: : Updated grid load rate; : Real-time power allocation for each user; Battery temperature and current power , to prevent the risk of overcharging or overheating.

[0078] If an abnormality is detected (e.g. or ), instant adjustment : ; : Safety temperature threshold (such as 40°C), : Critical temperature threshold (such as 50℃).

[0079] Output real-time monitoring results , including the dynamic changes of all key state variables, providing feedback guarantee for the safe operation of the whole system.

[0080] Furthermore, the actual power of all users , Energy feedback power and grid load Integrate into the final adjusted state: , output It is used for further analysis or status update, and the user's charging progress and system operating parameters are updated to enter the next scheduling cycle.

[0081] The embodiment of the present invention further provides a remote control system for a new energy vehicle charging pile, the system comprising: The state variable acquisition unit is used to collect the vehicle battery status, user demand, and the operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables; Wherein, the vehicle battery status includes the vehicle battery health status, current battery percentage and battery temperature; The user requirements include the target power level that the user expects to be charged, the charging completion time that the user expects, and the maximum charging power that the user can accept; The operation information of the charging pile includes the total power of the current charging pile and the grid load rate; A single-user charging strategy generating unit is used to calculate the initial charging power of a single user based on the comprehensive state variables, with the target power expected to be charged by the user and the charging completion time expected by the user as constraints, and then dynamically adjust the initial charging power of the single user according to the health status of the vehicle battery to obtain the adjusted charging power, further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate the single-user charging strategy and the single-user charging strategy set in combination with the user demand and the healthy adjusted power; A multi-user charging strategy generation unit is used to analyze the available allocated power in a multi-user scenario according to the dynamic load situation of the current power grid, calculate the user charging priority according to the allocated power combined with a dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculate the actual allocated power of each user according to the user's charging priority combined with the user's required power using a normalized weight allocation strategy, and verify the actual allocated power of each user, and integrate the actual allocated power allocation of each qualified user into a multi-user allocation strategy; wherein the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time and the user's final adjusted priority; The V2G bidirectional feedback control unit is used to receive the multi-user allocation strategy and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, its actual energy demand is calculated, and the user's charging time is calculated in combination with the user's actual charging power. In addition, the real-time power of each user is dynamically adjusted in combination with the operation information of the charging pile and the user's actual charging power. The V2G mechanism is introduced to update the real-time adjustment variable set to reflect the change in the grid load after the feedback execution of the adjustment variable set. The V2G mechanism is specifically: When the grid load exceeds the warning threshold, the V2G mode of the user's vehicle is triggered, and the actual feedback power Calculated according to the following formula: ; in, The safety time buffer required for feedback (in hours) to ensure the minimum battery level of the vehicle; is the current battery power percentage of user i, is the current remaining power, For users Total battery capacity; For users The power fed back to the grid by the vehicle depends on the current charge and minimum requirements; The actual allocated power for user i; Update real-time adjustment variable collection , reflecting the change of grid load after feedback: ; in, is the updated grid load factor, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging pile.

[0082] The real-time monitoring control unit is used to monitor the changes in the grid load in real time. If an abnormality is detected, the real-time power allocation of each user will be adjusted immediately, and the real-time monitoring results will be output for feedback, including the dynamic changes of all key state variables, integrating the real-time power allocation, energy feedback power and grid load of all users into the final adjustment state.

[0083] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0084] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0085] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0086] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A remote control method for a new energy vehicle charging pile, characterized in that: The method comprises the following steps: S1. Collect vehicle battery status, user needs, and operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables; Wherein, the vehicle battery status includes the vehicle battery health status, current battery percentage and battery temperature; The user requirements include the target power level that the user expects to be charged, the charging completion time that the user expects, and the maximum charging power that the user can accept; The operation information of the charging pile includes the total power of the current charging pile and the grid load rate; S2. Based on the comprehensive state variables, with the user's expected target power and the user's expected charging completion time as constraints, calculate the initial charging power of a single user, and then dynamically adjust the initial charging power of the single user according to the vehicle battery health status to obtain the adjusted charging power, and further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate the single user charging strategy and the single user charging strategy set in combination with the user demand and the healthy adjusted power; S3. Analyze the available allocated power in the multi-user scenario according to the dynamic load situation of the current power grid, calculate the user charging priority according to the single-user charging strategy set combined with the dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs. Then, according to the user's charging priority and the user's required power, a normalized weight allocation strategy is used to calculate the actual allocated power of each user, and the actual allocated power of each user is verified, and the actual allocated power allocation of each qualified user is integrated into a multi-user allocation strategy; wherein, the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time, and the user's final adjusted priority; S4. Receive the multi-user allocation strategy, and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, calculate its actual energy demand, and calculate the user's charging time in combination with the user's actual charging power. In combination with the operation information of the charging pile and the user's actual charging power, dynamically adjust the real-time power of each user, introduce the V2G mechanism to update the real-time adjustment variable set, and reflect the grid load changes after the feedback execution adjustment variable set. The V2G mechanism is specifically: When the grid load exceeds the warning threshold, the V2G mode of the user's vehicle is triggered, and the actual feedback power Calculated according to the following formula: ； in, To provide a safe time buffer for feedback and ensure the minimum battery level of the vehicle; is the current battery power percentage of user i, is the current remaining power, For users Total battery capacity; For users The power fed back to the grid by the vehicle depends on the current charge and minimum requirements; The actual allocated power for user i; Update real-time adjustment variable collection , reflecting the change of grid load after feedback: ； in, is the updated grid load factor, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging pile; S5. Monitor the changes in grid load in real time. If any abnormality is detected, adjust the real-time power allocation of each user immediately and output the real-time monitoring results for feedback, including the dynamic changes of all key state variables, and integrate the real-time power allocation, energy feedback power and grid load of all users into the final adjustment state.

2. A remote control method for a new energy vehicle charging pile according to claim 1, characterized in that: The S1 also includes cleaning the collected comprehensive state variables, eliminating abnormal values, and filling in missing values ​​through interpolation to ensure the integrity of the state variables, taking battery data as the main time axis, and aligning the power grid and user data through interpolation and matching methods.

3. A remote control method for a new energy vehicle charging pile according to claim 1, characterized in that: The calculation of the initial charging power of a single user , calculated as follows: ； in, The maximum charging power allowed by the user; is the current remaining power, is the total capacity of the battery; The user's expected completion time; For charging efficiency, The target power level that the user expects to charge; The initial charging power of a single user is dynamically adjusted according to the vehicle battery health status to obtain an adjusted charging power, It is expressed as: ； in, is the health status factor, through controlling Dynamically limit power, To adjust the parameters; Ensure that the minimum charging power is 50% of the normal value to avoid excessive derating that may cause a decrease in user experience; Finally, the single-user charging strategy is generated by combining user demand and health-adjusted power. : ； in, The final charging power is adjusted based on time requirements, health status and temperature factors; The completion time entered by the user is directly used for scheduling priority; is the user priority, initialized to , Small positive number to avoid zero value.

4. A remote control method for a new energy vehicle charging pile according to claim 3, characterized in that: Constrain the final charging power: If the battery temperature Approaching the critical threshold, further To perform dynamic derating: ； in, is the adjusted charging power, is the safe operating temperature threshold; is the critical temperature threshold; is the temperature adjustment coefficient.

5. A remote control method for a new energy vehicle charging pile according to claim 3, characterized in that: For the single user charging strategy Verify the following: Ensure the final charging power , The maximum charging power acceptable to the user should be avoided to avoid exceeding the power allowed by the user; Ensure the final charging power , The total available power of the charging pile should be avoided to exceed the single-port power limit of the charging pile.

6. A remote control method for a new energy vehicle charging pile according to claim 3, characterized in that: The available distribution power in the multi-user scenario is analyzed according to the dynamic load condition of the current power grid. , calculated as follows: ； in, is a nonlinear adjustment function, and is an empirical parameter that controls the impact of load on the distributed power; The dynamic weight adjustment model is expressed as: ； in, is the priority of user i, For users The cumulative charging time used; The user's goal completion time; To prevent small positive numbers with zero denominator; The actual allocated power of each user is calculated by using a normalized weight allocation strategy based on the user's charging priority and the user's required power. , expressed as: ； in, It is a sparse parameter used to prevent some users from occupying high-power resources for a long time; To normalize the square weight term, enhance the sparsity of high-weight users.

7. A remote control method for a new energy vehicle charging pile according to claim 6, characterized in that: Verify the actual allocated power for each user Do the following constraints apply? A. , ensuring that the total power does not exceed the allocated upper limit; B. For each user, , to avoid exceeding the user's hardware limitations; Then, the actual power allocation of each user is integrated into the multi-user allocation strategy : ,in, is the final adjusted dynamic priority.

8. A remote control method for a new energy vehicle charging pile according to claim 6, characterized in that: The S4 includes, for each user , calculate its actual energy demand , and combined with the actual allocated power of each user Calculate charging time: ； in, For users Target power; is the current remaining power, For users Total battery capacity; Combined with the grid status The actual power allocated to the user , dynamically adjust the real-time power of each user , introducing the grid load smoothing term : ； in, ,in is the smoothing coefficient, which is used to control the intensity of load smoothing.

9. A remote control method for a new energy vehicle charging pile according to claim 8, characterized in that: The S5 comprises: Monitor changes in key variables during operation, including: updated grid load rate, real-time power allocation for each user, battery temperature and current power ; If an abnormality is detected, or , instant adjustment : ； in, is the safety temperature threshold, is the critical temperature threshold.

10. A remote control system for a new energy vehicle charging pile, characterized in that: The system comprises: The state variable acquisition unit is used to collect the vehicle battery status, user demand, and the operation information of the power grid and charging piles, and integrate them into standardized comprehensive state variables; Wherein, the vehicle battery status includes the vehicle battery health status, current battery percentage and battery temperature; The user requirements include the target power level that the user expects to be charged, the charging completion time that the user expects, and the maximum charging power that the user can accept; The operation information of the charging pile includes the total power of the current charging pile and the grid load rate; A single-user charging strategy generating unit is used to calculate the initial charging power of a single user based on the comprehensive state variables, with the target power expected to be charged by the user and the charging completion time expected by the user as constraints, and then dynamically adjust the initial charging power of the single user according to the health status of the vehicle battery to obtain the adjusted charging power, further correct the adjusted charging power in combination with the battery temperature to obtain the healthy adjusted power, and then generate the single-user charging strategy and the single-user charging strategy set in combination with the user demand and the healthy adjusted power; A multi-user charging strategy generation unit is used to analyze the available allocated power in a multi-user scenario according to the dynamic load situation of the current power grid, calculate the user charging priority according to the single-user charging strategy set combined with a dynamic weight adjustment model based on time decay, analyze the impact of the cumulative charging time on the priority, and when the current user's cumulative charging time increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculate the actual allocated power of each user according to the user's charging priority combined with the user's required power using a normalized weight allocation strategy, and verify the actual allocated power of each user, and integrate the actual allocated power allocation of each qualified user into a multi-user allocation strategy; wherein the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time and the user's final adjusted priority; The V2G bidirectional feedback control unit is used to receive the multi-user allocation strategy and build a real-time adjustment variable set in combination with the operation information of the charging pile. Then, for each user, its actual energy demand is calculated, and the user's charging time is calculated in combination with the user's actual charging power. In addition, the real-time power of each user is dynamically adjusted in combination with the operation information of the charging pile and the user's actual charging power. The V2G mechanism is introduced to update the real-time adjustment variable set to reflect the change in the grid load after the feedback execution of the adjustment variable set. The V2G mechanism is specifically: When the grid load exceeds the warning threshold, the V2G mode of the user's vehicle is triggered, and the actual feedback power Calculated according to the following formula: ； in, To provide a safe time buffer for feedback and ensure the minimum battery level of the vehicle; is the current battery power percentage of user i, is the current remaining power, For users Total battery capacity; For users The power fed back to the grid by the vehicle depends on the current charge and minimum requirements; The actual allocated power for user i; Update real-time adjustment variable collection , reflecting the change of grid load after feedback: ； in, is the updated grid load factor, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging pile; The real-time monitoring control unit is used to monitor the changes in the grid load in real time. If an abnormality is detected, the real-time power distribution of each user will be adjusted immediately, and the real-time monitoring results will be output for feedback, including the dynamic changes of all key state variables, integrating the real-time power distribution, energy feedback power and grid load of all users into the final adjustment state.

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