A remote control method and system for a new energy vehicle charging pile

Abstract

The present invention provides a remote control method and system for a new energy vehicle charging pile. The method includes: collecting the vehicle battery status, user requirements, and the operation information of the power grid and the charging pile, and integrating them into a standardized comprehensive state variable; dynamically adjusting the initial charging power of a single user according to the vehicle battery health status to obtain the adjusted charging power; calculating the charging priority of the user by combining the power distribution 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 monitoring the change of the power grid load in real time. On the one hand, the present invention provides a flexible solution for the personalized needs of users. On the other hand, through energy sharing and remote dynamic control, the efficient utilization and active management of system resources are realized, thus overcoming the main defects of the prior art and having important technical value and application prospects.

Description

Technical Field

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

[0002] With the rapid popularization of new energy vehicles, charging piles, as one of their core infrastructure, are facing increasingly complex usage requirements and technical challenges. Currently, charging piles not only need to meet the basic charging function, but also need to achieve efficient coordination in aspects such as grid load pressure, vehicle battery health management, and multi-user concurrent requirements. However, the existing technologies have obvious defects in the following key aspects:

[0003] Single charging mode, difficult to meet diverse needs: Most existing charging piles provide fixed charging power and modes, and cannot be dynamically adjusted according to the specific needs of different users. For example, for users who need rapid charging, the charging efficiency may not be high enough; for users who focus on battery life maintenance, there is a lack of an adaptive charging strategy based on the State of Health (SOH) of the battery, which will accelerate the decline and loss of the battery. In addition, existing charging piles lack scene adaptability. For example, in commercial parking lots or multi-user sharing scenarios, they cannot reasonably allocate charging resources, easily resulting in some vehicles occupying resources for a long time while other vehicles cannot be charged in time.

[0004] Low energy utilization efficiency, 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 unidirectional power flow and cannot achieve bidirectional energy interaction between vehicles and the grid. In the case of peak grid load, new energy vehicles cannot be effectively utilized as distributed energy storage units to relieve grid pressure, and during off-peak hours, the charging power cannot be flexibly adjusted to reduce electricity costs or achieve efficient energy utilization.

[0005] Insufficient remote management and control functions: The existing remote control systems mainly focus on basic start and stop functions and lack in-depth real-time optimization capabilities. For example, in a multi-user scenario, it is impossible to effectively identify the urgency and demand priorities of different users to achieve intelligent resource allocation. At the same time, for abnormalities that may occur during the charging process (such as equipment overheating, current fluctuations), the fault handling of existing technologies is mostly passive response, lacking active prevention and real-time intervention capabilities. This defect leads to insufficient guarantee of both user experience and system stability.

[0006] In summary, the existing technologies cannot achieve efficient intelligent coordination among user needs, energy utilization, and system management. These defects limit the development of new energy vehicle charging piles, and there is an urgent need for a more intelligent and flexible remote control method and system. Summary of the Invention

[0007] The object of the present invention is to propose a fault diagnosis method and system for a new energy vehicle charging pile. On the one hand, it provides a flexible solution for the personalized needs of users. On the other hand, through energy sharing and remote dynamic control, it realizes the efficient utilization and active management of system resources, thus overcoming the main defects of the prior art and having important technical value and application prospects.

[0008] To achieve the above object, in the first aspect of the present invention, a remote control method for a new energy vehicle charging pile is provided. The method includes the following steps:

[0009] S1. Collect the vehicle battery status, user requirements, and the operating information of the power grid and the charging pile, and integrate them into a standardized comprehensive state variable;

[0010] Among them, the vehicle battery status includes the vehicle battery health status, the current battery charge percentage, and the battery temperature;

[0011] The user requirements include the target power that the user expects to charge to, the user's expected charging completion time, and the maximum charging power acceptable to the user;

[0012] The operating information of the charging pile includes the total power of the current charging pile and the power grid load rate;

[0013] S2. Based on the comprehensive state variable, with the target power that the user expects to charge to and the user's expected charging completion time as constraints, calculate the initial charging power for a single user, then dynamically adjust the initial charging power for a single user according to the vehicle battery health status to obtain the adjusted charging power, further correct the adjusted charging power in combination with the battery temperature to obtain the power after health adjustment, and then combine the user requirements and the power after health adjustment to generate a single user charging strategy and a set of single user charging strategies;

[0014] S3. Analyze the available allocation power in a multi-user scenario according to the dynamic load situation of the current power grid, calculate the charging priority of users according to the allocated power in combination with a dynamic weight adjustment model based on time decay, analyze the influence of the cumulative charging time on the priority. When the cumulative charging time of the current user increases, the corresponding priority will gradually decrease, ensuring that resources are tilted towards users with more urgent needs. Then, calculate the actual allocated power of each user according to the charging priority of the user in combination with the required power of the user using a normalized weight allocation strategy, and verify the actual allocated power of each user. Integrate the actual allocated power of each user that meets the conditions into a multi-user allocation strategy; among them, the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time, and the user's finally adjusted priority;

[0015] S4. Receive the multi-user allocation strategy, construct a real-time adjustment variable set in combination with the operating information of the charging piles. Then, for each user, calculate their actual energy demand, calculate the charging time of the user in combination with the actual charging power of the user, and dynamically adjust the real-time power of each user in combination with the operating information of the charging piles and the actual charging power of the user. Introduce the V2G mechanism to update the real-time adjustment variable set to reflect the change in the grid load after feedback and execution of the adjustment variable set.

[0016] Among them, the V2G mechanism is specifically:

[0017] When the grid load exceeds the warning threshold, trigger the V2G mode of the user's vehicle, and the actual feedback power is calculated according to the following formula:

[0018] ;

[0019] Among them, is the safety time buffer required for feedback (unit: hour) to ensure the minimum battery level of the vehicle; is the percentage of the current battery charge of user i, is the current remaining charge, is the user 's total battery capacity; is the user The power fed back by the vehicle to the grid depends on the current battery charge and the minimum requirement; is the actual allocated power of user i;

[0020] Update the real-time adjustment variable set , reflecting the change in the grid load after feedback:

[0021] ;

[0022] Among them, is the updated grid load rate, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging piles.

[0023] S5. Monitor the change in the grid load in real time. If an abnormality is detected, immediately adjust the real-time power allocation of each user and output the real-time monitoring result for feedback, including the dynamic changes of all key state variables. Integrate the real-time power allocation, energy feedback power, and grid load of all users into the final adjustment state.

[0024] Furthermore, S1 also includes cleaning the collected comprehensive state variables, removing outliers, and filling in the missing values by interpolation to ensure the integrity of the state variables. Align the grid and user data by interpolation and matching methods with the battery data as the main time axis.

[0025] Furthermore, calculate the initial charging power of a single user , and the calculation is as follows:

[0026] ;

[0027] Wherein, is the maximum charging power allowed by the user; is the current remaining battery level, is the total battery capacity; is the user's desired completion time; is the charging efficiency, is the target battery level that the user desires to charge to;

[0028] The adjusted charging power is obtained by dynamically adjusting the initial charging power of a single user according to the health status of the vehicle battery, which is expressed as:

[0029] ;

[0030] Wherein, is the health status factor, and the power is dynamically limited by controlling ; is the adjustment parameter; Ensure that the minimum charging power is 50% of the normal value to avoid a decrease in the user experience caused by excessive derating;

[0031] Finally, combine the power adjusted according to the user's needs and health to generate a single-user charging strategy :

[0032] ;

[0033] Wherein, is the final charging power, adjusted considering the time requirement, health status, and temperature factors; is the completion time input by the user, which is directly used for scheduling priority; is the user priority, initialized to , is a small positive number to avoid zero value.

[0034] Furthermore, impose constraints on the final charging power:

[0035] If the battery temperature is close to the critical threshold, further dynamically derate :

[0036] ;

[0037] Wherein, is the adjusted charging power, is the safe operating temperature threshold; is the critical temperature threshold; is the temperature adjustment coefficient.

[0038] Furthermore, the single-user charging strategy is verified as follows:

[0039] Ensure that the final charging power , is the maximum charging power acceptable to the user, and avoid exceeding the power allowed by the user;

[0040] Ensure that the final charging power , is the total available power of the charging pile, and avoid exceeding the single-port power limit of the charging pile.

[0041] Furthermore, the available allocation power in the multi-user scenario is analyzed according to the dynamic load situation of the current power grid , and the calculation is as follows:

[0042] ;

[0043] wherein, is a non-linear adjustment function, and are empirical parameters that control the influence of the load on the allocated power;

[0044] The dynamic weight adjustment model is expressed as:

[0045] ;

[0046] wherein, is the priority of user i, is the user cumulative charging time already used; is the target completion time of the user; is a small positive number to prevent the denominator from being zero;

[0047] Then, according to the priority of user charging and the required power of the user, the normalized weight allocation strategy is used to calculate the actual allocated power of each user , which is expressed as:

[0048] ;

[0049] wherein, is a sparsity parameter used to prevent some users from occupying high-power resources for a long time; is the normalized squared weight term to enhance the sparsity of high-weight users.

[0050] Further, verify the actual allocated power of each user to check whether it meets the following constraints:

[0051] A. to ensure that the total power does not exceed the allocation limit;

[0052] B. For each user, to avoid exceeding the user's hardware limit;

[0053] Then, integrate the actual power allocation of each user into a multi - user allocation strategy :

[0054] where, is the dynamically adjusted dynamic priority after final adjustment.

[0055] Further, the said S4 includes, for each user calculate its actual energy demand and combine it with the actual allocated power of each user to calculate the charging time:

[0056] ;

[0057] where, is the target power of user (unit: kWh); is the current remaining power, is the user 's total battery capacity;

[0058] Combined with the grid status and the actual allocated power of the user dynamically adjust the real - time power of each user introduce a grid load smoothing term :

[0059] ;

[0060] where, where is the smoothing coefficient, used to control the intensity of load smoothing.

[0061] Further, the said S5 includes:

[0062] Monitor the changes of key variables during the operation process, mainly including: the updated grid load rate, the real - time power allocation of each user, the battery temperature and the current power ;

[0063] If an abnormality is monitored, that is or for immediate adjustment :

[0064] ;

[0065] wherein is the safety temperature threshold is the critical temperature threshold

[0066] In a second aspect of the present invention, a remote control system for a new energy vehicle charging pile is provided, and the system includes:

[0067] A state variable acquisition unit for acquiring the vehicle battery state, user requirements, and the operating information of the power grid and the charging pile, and integrating them into a standardized comprehensive state variable;

[0068] wherein, the vehicle battery state includes the vehicle battery health state, the current battery power percentage, and the battery temperature;

[0069] The user requirements include the target power that the user expects to charge to, the user's expected charging completion time, and the maximum charging power acceptable to the user;

[0070] The operating information of the charging pile includes the total power of the current charging pile and the power grid load rate;

[0071] A single-user charging strategy generation unit for calculating the initial single-user charging power based on the comprehensive state variable, with the target power that the user expects to charge to and the user's expected charging completion time as constraints, then dynamically adjusting the initial single-user charging power according to the vehicle battery health state to obtain the adjusted charging power, further correcting the adjusted charging power in combination with the battery temperature to obtain the power after health adjustment, and then generating a single-user charging strategy and a set of single-user charging strategies in combination with the user requirements and the power after health adjustment;

[0072] A multi-user charging strategy generation unit for analyzing the available allocated power in a multi-user scenario according to the dynamic load condition of the current power grid, calculating the charging priority of users according to the allocated power in combination with a dynamic weight adjustment model based on time decay, analyzing the influence of the cumulative charging time on the priority, and when the cumulative charging time of the current user increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs, and then calculating the actual allocated power of each user using a normalized weight allocation strategy in combination with the required power of the users, and verifying the actual allocated power of each user, and integrating and allocating the actual allocated power of each user that meets the conditions 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 finally adjusted priority;

[0073] The V2G bi-directional feedback control unit is used to receive the multi-user allocation strategy, construct a real-time adjustment variable set in combination with the operation information of the charging piles, and then for each user, calculate its actual energy demand, calculate the charging time of the user in combination with the actual charging power of the user, and dynamically adjust the real-time power of each user in combination with the operation information of the charging piles and the actual charging power of the user, introduce the V2G mechanism to update the real-time adjustment variable set, and reflect the change in the grid load after the feedback execution adjustment variable set;

[0074] Among them, the V2G mechanism is specifically:

[0075] When the grid load exceeds the warning threshold, trigger the V2G mode of the user vehicle, and the actual feedback power is calculated according to the following formula:

[0076] ;

[0077] Among them, is the safety time buffer required for feedback (unit: hour) to ensure the minimum battery level of the vehicle; is the percentage of the current battery charge of user i, is the current remaining battery charge, is the user 's total battery capacity; is the user The power fed back by the vehicle to the grid depends on the current battery charge and the minimum requirement; is the actual allocated power of user i;

[0078] Update the real-time adjustment variable set , reflecting the change in the grid load after the feedback:

[0079] ;

[0080] Among them, is the updated grid load rate, is the current grid load rate, N is the total number of users, i is user i, is the total power of the charging piles.

[0081] The real-time monitoring control unit is used to monitor the change in the grid load in real time. If an abnormality is detected, immediately adjust the real-time power allocation of each user, and output the real-time monitoring result 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.

[0082] The beneficial technical effects of the present invention are at least as follows:

[0083] The present invention dynamically adjusts the charging power output by collecting the state of health (SOH) and state of charge (SOC) of the vehicle battery in real time, and combining user requirements and a battery loss model. This strategy can provide a "health priority" mode or a "fast charging" mode according to the needs of different users, extend the battery life and optimize the charging efficiency, fundamentally solving the problem of a single charging mode in the prior art.

[0084] The V2G and G2V bi-directional charging technologies are introduced to control the energy flow between the vehicle and the power grid through a remote platform. During peak grid loads, the vehicle is allowed to feed back excess power to the grid; during off-peak periods, the charging power is automatically adjusted to achieve low-cost charging. In addition, by optimizing the algorithm to allocate charging resources in a multi-user sharing scenario, the overall energy utilization efficiency of the charging pile is improved, solving the problems of unidirectional energy flow and uneven resource allocation in the prior art.

[0085] A set of automatic switching mechanisms for charging modes based on scenario awareness is designed. The remote management platform can automatically identify the charging scenario and select the best mode according to the vehicle status, user reservation information, and grid load conditions. For example, the "balanced mode" can be enabled in a commercial parking lot to optimize the resource allocation for multiple users; the "energy-saving mode" can be provided for personal home use to extend the battery life. In addition, through the abnormal detection and fault warning functions, the remote management platform can actively identify potential problems and take intervention measures, significantly improving the safety and stability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The present invention is further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation to the present invention. For those of ordinary skill in the art, other drawings can also be obtained according to the following drawings without creative efforts.

[0087] Figure 1 It is a flowchart of a remote control method for a new energy vehicle charging pile according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0088] The embodiments of the present invention are described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0089] As Figure 1 shown, a remote control method for a new energy vehicle charging pile provided by an embodiment of the present invention includes:

[0090] S1. Collect the vehicle battery status, user requirements, and the operating information of the power grid and the charging pile, and integrate them into a standardized comprehensive state variable.

[0091] Specifically, using the vehicle's charging communication protocol (such as ISO15118), obtain the vehicle battery status data in real time from the charging interface:

[0092] : State of Health, the battery health status, representing the ratio of the battery capacity to a new battery (range: 0 - 1).

[0093] : State of Charge, the percentage of the current battery charge (range: 0 - 100).

[0094] : Battery temperature (unit: °C).

[0095] Perform time synchronization on the collected data to ensure data consistency, for example, serialize the data stream using timestamps.

[0096] Among them, user demand information collection and structuring:

[0097] The user inputs the following information through the remote interface:

[0098] : The target power (unit: kWh) that the user expects to charge to.

[0099] : The expected charging completion time (unit: minutes) of the user.

[0100] : The maximum charging power (unit: kW) acceptable to the user.

[0101] Store this information as a user demand vector: .

[0102] Initialize the user priority flag for multi - user scheduling:

[0103]

[0104] Furthermore, synchronize the charging pile status and grid information:

[0105] The charging pile terminal collects and uploads the following information in real time:

[0106] : The total available power of the charging pile (unit: kW).

[0107] : The grid load condition, representing the load rate of the current line (range: 0 - 1).

[0108] Furthermore, integrate this information into grid state variables:

[0109] ;

[0110] Furthermore, integrate into a comprehensive state variable: Integrate vehicle, grid, and user data into a unified set of state variables:

[0111] ;

[0112] : State of health of the vehicle battery.

[0113] : Demand information of the user.

[0114] : Operating states of the grid and charging piles.

[0115] Furthermore, data consistency verification and cleaning:

[0116] For the collected Perform cleaning to remove outliers.

[0117] Fill in the missing values through interpolation to ensure the integrity of the state variables. For example: ;

[0118] Data synchronization processing: Using the battery data as the main time axis, align the grid and user data through interpolation and matching methods.

[0119] Furthermore, output variables:

[0120] Organize into a standardized input for the subsequent steps: ;

[0121] The definition of each sub-variable is consistent with the subsequent steps and serves as the input for the generation and optimization of subsequent strategies.

[0122] S2. Based on the comprehensive state variable, with the target power that the user expects to charge to and the user-expected charging completion time as constraints, calculate the initial charging power of a single user, then dynamically adjust the initial charging power of the single user according to the state of health 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 power after health adjustment, and then combine the user demand and the power after health adjustment to generate a single-user charging strategy and a set of single-user charging strategies.

[0123] Specifically, receive the comprehensive state variable output in step 1 , and clarify its components:

[0124] : State of health of the vehicle battery ( ), current battery percentage ( ), battery temperature ( );

[0125] : The target power of the user ( ), the expected charging completion time ( ), the maximum acceptable power ( );

[0126] : The total power of the current charging pile ( ) and the grid load rate ( ).

[0127] Provide the basic data environment for each user through the above input, and ensure the consistency of these data in the subsequent steps.

[0128] Furthermore, calculate the initial charging power of a single user:

[0129] Considering the user's needs and the current state of the battery, design an initial power calculation formula based on time priority:

[0130] ;

[0131] : The maximum charging power allowed by the user (unit: kW);

[0132] : The current remaining power, is the total battery capacity (unit: kWh);

[0133] : The user's expected completion time (unit: hours);

[0134] : Charging efficiency (0.9 - 1.0, dynamically obtained or set as a fixed value).

[0135] In this formula, directly use the user's target power and expected completion time as constraint conditions to ensure that the calculated power meets the time priority requirements and is operable.

[0136] Furthermore, adjust the power to protect the battery health:

[0137] To protect the battery life, dynamically adjust the initial power according to the battery health status to obtain the adjusted charging power : :

[0138] ;

[0139] : Health status factor, by controlling ( As a regulation parameter, it is usually set to 0.8 - 1.2) to dynamically limit the power;

[0140] : Ensure that the minimum charging power is 50% of the normal value to avoid the degradation of user experience caused by excessive derating.

[0141] In this formula, an innovative non - linear adjustment term is introduced. By optimizing the value, the power protection balance for batteries in different health states is achieved.

[0142] Furthermore, a health protection mechanism with a temperature constraint term is introduced:

[0143] If the battery temperature is close to the critical threshold (e.g., ), further dynamic derating is performed on :

[0144]

[0145] : The safe operating temperature threshold (e.g., );

[0146] : The critical temperature threshold (e.g., );

[0147] : The temperature adjustment coefficient (usually set to 0.1 - 0.5).

[0148] This formula minimizes the threat of temperature changes to battery life through the quadratic adjustment of power by temperature.

[0149] Finally, combining the user's needs and the power adjusted for health, a single - user charging strategy is generated:

[0150] ;

[0151] : The final charging power, adjusted considering time requirements, health status, and temperature factors;

[0152] : The completion time input by the user, directly used for scheduling priority;

[0153] : The user priority, initialized to ( is a small positive number to avoid zero).

[0154] Furthermore, verification and output:

[0155] For the generated perform the following verifications:

[0156] Ensure , and avoid exceeding the power allowed by the user;

[0157] Ensure , and avoid exceeding the single-port power limit of the charging pile.

[0158] Output the complete charging strategy for a single user , including the power and priority that meet multiple constraint conditions.

[0159] S3. Analyze the available allocated power in a multi-user scenario according to the dynamic load situation of the current power grid, calculate the charging priority of users according to the allocated power combined with the dynamic weight adjustment model based on time decay, analyze the influence of the cumulative charging time on the priority. When the cumulative charging time of the current user increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs. Then, calculate the actual allocated power of each user using the normalized weight allocation strategy according to the charging priority of the user combined with the user's required power, and verify the actual allocated power of each user. Integrate the actual allocated power of each eligible user into the multi-user allocation strategy; among them, the multi-user allocation strategy includes the actual charging power allocated to the user, the user's target completion time, and the user's finally adjusted priority.

[0160] Specifically, receive the set of single-user strategies generated in step 2 , where each user strategy is:

[0161] ;

[0162] : The finally adjusted charging power (unit: kW) of user , adjusted by time constraint and health protection;

[0163] : The target completion time (unit: minutes) of user , indicating the urgency of the user's demand;

[0164] : The priority of user , calculated by step 2 based on the completion time and the importance of the demand.

[0165] Receive the charging pile status and power grid information from step S1 ;

[0166] : Total available power of the charging pile (unit: kW), providing a hard limit for distribution;

[0167] : Grid load rate (range: 0 - 1). When the load is high, the distributed power needs to be reduced to prevent system overload.

[0168] Furthermore, all user policies and system states are integrated into a global allocation status variable , which is used to uniformly describe the context of resource allocation:

[0169] Furthermore, calculate the global allocation power limit:

[0170] In a multi - user scenario, according to dynamically calculate the available allocation power , introducing an innovative grid load adjustment factor :

[0171] ;

[0172] : Non - linear adjustment function, and are empirical parameters that control the impact of the load on the allocated power;

[0173] Example: When (grid load 50%), (assuming ), then . This formula introduces non - linear reduction when the grid load is high, effectively alleviating peak pressure.

[0174] Furthermore, based on the traditional priority, design a time - decaying dynamic weight adjustment model , introducing "the impact of cumulative charging time on priority":

[0175] ;

[0176] : User cumulative charging time already used;

[0177] : User's target completion time;

[0178] : A small positive number to prevent the denominator from being zero.

[0179] When the cumulative charging time of user increases, its priority will gradually decrease, ensuring that resources are tilted towards users with more urgent needs.

[0180] Furthermore, based on the dynamic weight of the user and the required power , a normalized weight allocation strategy is adopted to calculate the actual allocated power for each user . Meanwhile, an innovative sparse regularization term is introduced :

[0181] ;

[0182] : Sparsity parameter, used to prevent some users from occupying high-power resources for a long time;

[0183] : Normalized squared weight term, enhancing the sparsity of high-weight users.

[0184] This formula ensures that power allocation takes into account both demand priority and balances the allocation fairness through the sparse regularization term.

[0185] Furthermore, verify and allocate the policy output:

[0186] Verify whether the actually generated power allocation meets the following constraints:

[0187] , ensuring that the total power does not exceed the allocation upper limit;

[0188] For each user, , avoiding exceeding the user hardware limit.

[0189] Integrate the actual power allocation of each user into a multi-user allocation policy: , : The finally adjusted dynamic priority, used for subsequent updates and feedback. Output , providing input for the next-step real-time energy regulation.

[0190] Furthermore, record the comparison situation between the allocation result and as a real-time feedback indicator of system load and user satisfaction; by dynamically adjusting the parameter , optimize the flexibility and robustness of the overall allocation model.

[0191] S4. Receive the multi-user allocation strategy, construct a real-time adjustment variable set in combination with the operating information of the charging piles, then for each user, calculate their actual energy demand, calculate the charging time of the user in combination with the actual charging power of the user, and dynamically adjust the real-time power of each user in combination with the operating information of the charging piles and the actual charging power of the user. Introduce the V2G mechanism to update the real-time adjustment variable set and reflect the change in the grid load after feedback and execution of the adjustment variable set.

[0192] Specifically, input the multi-user allocation strategy set of step 3 , where:

[0193] ;

[0194] : The actual charging power allocated to user (unit: kW);

[0195] : The target completion time of user ;

[0196] : The finally adjusted priority of user ;

[0197] Receive the system state variables from step S1 :

[0198] : The total power of the charging piles (unit: kW);

[0199] : The grid load rate (range: 0 - 1).

[0200] Integrate the above inputs into a real-time adjustment variable set , providing a unified data environment for energy regulation and status feedback:

[0201] ;

[0202] Furthermore, for each user , calculate their actual energy demand (unit: kWh), and calculate the charging time in combination with the allocated power :

[0203] ;

[0204] : The target electricity quantity of user (unit: kWh);

[0205] : The current remaining electricity quantity For the user total battery capacity.

[0206] Combined with the grid status and the actual power allocated to the user , dynamically adjust the real-time power of each user , introduce an innovative grid load smoothing term :

[0207] ;

[0208] , where is the smoothing coefficient, used to control the intensity of load smoothing.

[0209] This formula ensures that the power output is gradually reduced under high load conditions to ensure grid stability.

[0210] Furthermore, introduce the vehicle-to-grid (V2G) energy feedback mechanism:

[0211] When the grid load exceeds the warning threshold (e.g., 0.8), trigger the vehicle-to-grid (V2G) energy feedback mode of the user's vehicle. The actual feedback power is calculated according to the following formula:

[0212] ;

[0213] : The safety time buffer required for feedback (unit: hour), to ensure the minimum battery level of the vehicle;

[0214] : The user The power fed back from the vehicle to the grid depends on the current battery level and the minimum requirement.

[0215] Update the real-time adjustment variable , reflecting the change in grid load after feedback:

[0216] ;

[0217] S5. Monitor the change of grid load in real time. If an anomaly is detected, immediately adjust the real-time power distribution of each user, and output the real-time monitoring results for feedback, including the dynamic changes of all key state variables, and integrate the real-time power distribution of all users, the energy feedback power and the grid load into the final adjustment state.

[0218] Specifically, monitor the changes of key variables during the operation of the monitoring system, mainly including:

[0219] : The updated grid load rate;

[0220] : The real-time power distribution for each user;

[0221] Battery temperature and the current battery level , to prevent the risks of overcharging or overheating.

[0222] If an anomaly is monitored (such as or ), immediately adjust :

[0223] ;

[0224] : The safe temperature threshold (such as 40°C), : The critical temperature threshold (such as 50°C).

[0225] Output the real-time monitoring results , including the dynamic changes of all key state variables, to provide feedback guarantee for the safe operation of the entire system.

[0226] Furthermore, integrate the actual power of all users , the energy feedback power and the grid load into the final adjustment state: , output for subsequent further analysis or status update, and at the same time update the charging progress of users and the system operation parameters, and enter the next scheduling cycle.

[0227] The embodiment of the present invention also provides a remote control system for a new energy vehicle charging pile, and the system includes:

[0228] A state variable acquisition unit, which is used to collect the vehicle battery state, user requirements, and the operation information of the grid and the charging pile, and integrate them into standardized comprehensive state variables;

[0229] Among them, the vehicle battery state includes the vehicle battery health state, the current battery percentage, and the battery temperature;

[0230] The user requirements include the target battery level that the user expects to charge to, the user's expected charging completion time, and the maximum charging power acceptable to the user;

[0231] The operation information of the charging pile includes the total power of the current charging pile and the grid load rate;

[0232] A single - user charging strategy generation unit, which is used to calculate the initial charging power of a single user based on the comprehensive state variable, with the target power that the user expects to charge to and the charging completion time expected by the user as constraints, then dynamically adjust the initial charging power of the single user according to the health state 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 power after health adjustment, and then combine the user's needs and the power after health adjustment to generate a single - user charging strategy and a single - user charging strategy set;

[0233] A multi - user charging strategy generation unit, which 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 charging priority of users according to the allocated power in combination with a dynamic weight adjustment model based on time decay, analyze the influence of the cumulative charging time on the priority. When the cumulative charging time of the current user increases, the corresponding priority will gradually decrease to ensure that resources are tilted towards users with more urgent needs. Then, according to the charging priority of users and the required power of users, 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 of each user that meets the conditions is allocated and integrated into a multi - user allocation strategy; among them, the multi - user allocation strategy includes the actual charging power allocated to the user, the user's target completion time, and the user's finally adjusted priority;

[0234] A V2G bidirectional feedback control unit, which is used to receive the multi - user allocation strategy, construct 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, calculate the charging time of the user in combination with the actual charging power of the user, and dynamically adjust the real - time power of each user in combination with the operation information of the charging pile and the actual charging power of the user, introduce the V2G mechanism to update the real - time adjustment variable set, and reflect the change of the power grid load after feeding back and executing the adjustment variable set;

[0235] Among them, the V2G mechanism is specifically:

[0236] When the power grid load exceeds the warning threshold, trigger the V2G mode of the user's vehicle, and the actual feedback power is calculated according to the following formula:

[0237] ;

[0238] Among them, is the safety time buffer required for feedback (unit: hour) to ensure the minimum battery level of the vehicle; is the percentage of the current battery power of user i, is the current remaining power, is user 's total battery capacity; is user The power fed back by the vehicle to the power grid depends on the current power level and the minimum requirement; The actual allocated power for user i;

[0239] Update the set of real-time adjustment variables , reflecting the change in the power grid load after feedback:

[0240] ;

[0241] Among them, is the updated power grid load rate, is the current power grid load rate, N is the total number of users, i is user i, is the total power of the charging piles.

[0242] The real-time monitoring and control unit is used to monitor the change in the power grid load in real time. If an abnormality is detected, it immediately adjusts the real-time power allocation of each user and outputs the real-time monitoring result for feedback, including the dynamic changes of all key state variables, and integrates the real-time power allocation, energy feedback power, and power grid load of all users into the final adjustment state.

[0243] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0244] In several embodiments provided by 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 merely illustrative. For example, the division of the units is only a logical function division, and there can be other division methods in actual implementation. For example, 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 displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be in electrical, mechanical, or other forms.

[0245] When the above-mentioned 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 this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs.

[0246] Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. 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: Calculating 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, The dynamic priority after final adjustment.

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 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.

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