Intelligent charging path planning and power grid load balancing method and system for ige vehicle fleet

Abstract

The present application relates to the technical field of intelligent scheduling, in particular to an IGV fleet intelligent charging path planning and power grid load balancing method and system, comprising collecting IGV battery voltage / charge sequence, identifying attenuation reversal points and measuring stable power supply duration, predicting attenuation mutation, constructing and correcting pre-charging window, simulating charging segmentation to extract features, matching power grid load climbing rate to mark resonance segments, combining task remaining duration and coordinates to determine network delay, and generating charging start instruction. In the present application, the battery operation data is collected to capture the energy attenuation trend reversal point, the stable power supply duration is quantified to accurately predict the performance mutation time, the pre-charging time window containing safety redundancy is constructed, the whole process power curve is simulated based on the charging parameters, the power climbing form features are extracted and compared with the real-time power grid load climbing rate, the risk period that may cause load resonance is identified, and the charging access time is dynamically shifted under the premise of ensuring that the task connection is not affected.

Description

Technical Field

[0001] This invention relates to the field of intelligent dispatching technology, and in particular to a method and system for intelligent charging route planning and power grid load balancing for IGV fleets. Background Technology

[0002] The field of intelligent scheduling algorithm technology involves core aspects such as task allocation, trajectory generation, charging management, and energy consumption balance for multi-entity equipment. Its overall content covers state acquisition, feature extraction, constraint modeling, and path generation under multi-source environmental information, as well as the overall calculation of fleet operation sequence, position relationship, remaining power, and operation requirements in dynamic scenarios to achieve coordinated scheduling of resources and tasks.

[0003] The IGV fleet intelligent charging path planning and grid load balancing method refers to, under the premise of knowing the fleet size, charging station distribution, vehicle remaining power, and work tasks, statically measuring the distance from each vehicle's current location to the reachable charging point, generating driving paths according to the distance order, determining whether a vehicle needs to enter the charging process based on its power threshold, and determining the queuing charging order of each vehicle in a first-come, first-served manner after entering the process. At the same time, the charging time slices of all vehicles are directly superimposed to obtain the grid load curve, and the load peak is reduced in a segmented manner.

[0004] Existing technologies determine charging demand solely based on static distance and fixed power thresholds, ignoring the actual impact of battery dynamic degradation characteristics on range under continuous operation. The first-come-first-served queuing mechanism results in random charging access times, making it easy for high-power loads to be concentrated during peak grid load periods. Simple load superposition cannot predict the coupling relationship between charging power changes and grid load fluctuations, increasing the risk of local grid impact. Furthermore, there is a lack of flexible planning for charging time windows in conjunction with subsequent operation tasks, making it difficult to effectively balance grid load balancing needs while ensuring transport capacity. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing an intelligent charging route planning and grid load balancing method and system for IGV fleets.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an IGV fleet intelligent charging route planning and grid load balancing method, comprising the following steps:

[0007] S1: Collect the battery voltage sequence and battery charge sequence of the on-board battery during the continuous operation cycle of the IGV to determine the reversal point of the battery energy decay trend and measure the stable battery power supply time.

[0008] S2: Based on the latest decay trend reversal point and the battery power supply stability duration, predict the next decay change time and construct an initial pre-charge window. Combine the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging pile to calculate the expected terminal state value and correct the initial pre-charge window to obtain the corrected pre-charge window.

[0009] S3: Simulate the vehicle charging process based on the expected terminal status value and the preset standard charging control parameters, divide the charging power segment into multiple charging power segments, and extract the power segment morphological feature set;

[0010] S4: Collect the real-time total power load sequence at the regional power grid connection point, calculate the current power grid load ramp-up rate, compare it with the power segment morphology feature set, and mark the load resonance segment;

[0011] S5: Obtain the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, define the network access delay interval, and make a joint judgment based on the load resonance segment and the corrected pre-charge window to generate a charging start command.

[0012] As a further aspect of the present invention, the battery power supply stability duration is specifically calculated as the period obtained by measuring the time span between two adjacent points where the decay trend reverses. The corrected pre-charge window includes the window opening time starting from the current time and the window closing time determined by shifting forward based on the expected terminal state value. The power segment morphology feature set includes a sequence of independent charging power segments cut according to the inflection point of the power change rate and the power amplitude increase rate corresponding to the charging power segment. The load resonance segment includes a power segment identifier that is consistent with the direction of change of the grid load climbing rate and the absolute deviation between the power amplitude increase rate and the grid load climbing rate. The charging start command includes a locked charging start time that falls within the time range of the corrected pre-charge window and a target charging pile location code selected based on the principle of minimizing driving power consumption.

[0013] As a further aspect of the present invention, the step of obtaining the stable battery power supply duration specifically includes:

[0014] S111: During the operation cycle of the IGV performing continuous operation tasks, the battery voltage sampling sequence and battery power sampling sequence of the vehicle battery are collected in real time according to the preset sampling frequency. The voltage drop amplitude of adjacent sampling time is calculated for the battery voltage sampling sequence, and the power difference of adjacent sampling time is calculated for the battery power sampling sequence. The battery energy decay rate is calculated based on the voltage drop amplitude and the power difference.

[0015] S112: Real-time monitoring of whether the battery energy decay rate is within a preset stable range. When the battery energy decay rate is detected to jump out of the stable range and suddenly change to the acceleration range, the moment of the sudden change is captured as the point of reversal of the decay trend.

[0016] S113: Arrange the decay trend reversal points in chronological order of occurrence, select two adjacent decay trend reversal points, extract the marker time corresponding to the latter decay trend reversal point and the marker time corresponding to the former decay trend reversal point respectively, calculate the time difference between the two marker times, and define the time difference as the battery power supply stable duration.

[0017] As a further aspect of the present invention, the step of obtaining the modified pre-charge window specifically includes:

[0018] S211: Extract the time position corresponding to the latest attenuation trend reversal point, sum it with the battery power supply stability time, predict the time when the next attenuation change will occur, extract the time period from the current time to the predicted time of the next attenuation change, define it as the time range of the initial planned charging task, and construct the initial pre-charging window.

[0019] S212: Obtain the current location coordinates of the IGV and the location coordinates of each idle charging pile, calculate the corresponding driving distance, obtain the vehicle's average energy consumption, convert the driving distance into the driving power consumption to the charging pile, calculate the voltage drop and power drop caused by the driving power consumption, calculate the difference between the sampled battery voltage and battery power at the current moment and the corresponding voltage drop and power drop, and obtain the expected terminal state value.

[0020] S213: Compare the expected terminal state value with the state threshold corresponding to the preset decay trend reversal point. When the expected terminal state value is detected to be lower than the corresponding state threshold, it is determined that the current battery state cannot support the predicted sudden change time. The end time parameter of the initial pre-charge window is forward-shifted to obtain the corrected pre-charge window.

[0021] As a further aspect of the present invention, the step of obtaining the power segment morphological feature set specifically includes:

[0022] S311: The expected terminal state value is used as the starting power and voltage conditions for the simulated charging operation. Substitute it into the preset standard charging control parameters to simulate the operation process of the vehicle continuously charging from the current expected terminal state value until it is restored to a fully charged state, and construct the expected charging power curve.

[0023] S312: Calculate the rate of change of charging power over time in the expected charging power curve, monitor the alternation of positive and negative signs in the power change rate, locate the moment when the power change rate reverses from positive to negative and mark it as the inflection point, use all inflection points as the boundary limits for curve segmentation, and cut the expected charging power curve into multiple independent time-power data intervals to obtain charging power segments.

[0024] S313: For each of the segmented charging power segments, extract the power amplitude corresponding to the start and end positions within the segment, calculate the power amplitude increase rate in combination with the time span covered by the segment, and summarize the power amplitude increase rate of each segment in chronological order to establish a power segment morphological feature set.

[0025] As a further aspect of the present invention, the step of obtaining the load resonance segment specifically includes:

[0026] S411: Real-time monitoring and collection of the real-time total power load sequence at the regional power grid connection point. Based on the preset sliding time window duration, extract the current moment and a preset number of continuous sampling points from the real-time total power load sequence as calculation samples. Call the least squares method to perform linear fitting on the load within the calculation samples to obtain the power grid load ramp rate.

[0027] S412: Extract the power amplitude increase rate corresponding to each charging power segment in the power segment morphological feature set one by one, compare the positive and negative signs of the grid load increase rate and each power amplitude increase rate to determine the consistency of the change direction, calculate the difference between the grid load increase rate and the power amplitude increase rate with the same change direction, and generate the rate deviation amplitude value.

[0028] S413: Compare the rate deviation amplitude value with a preset matching threshold, filter data entries whose rate deviation amplitude value is less than the preset matching threshold, trace the index position of the corresponding charging power segment in the original expected charging power curve, and establish a load resonance segment.

[0029] As a further aspect of the present invention, the step of obtaining the charging start command specifically includes:

[0030] S511: Obtain vehicle operation task scheduling information, extract the remaining working time of the current task and the coordinates of the next work area, calculate the return distance from the charging pile to the next work area after charging is completed by combining the coordinates of the location of the idle charging pile, obtain the vehicle's average energy consumption parameters and convert the return distance into return power consumption, calculate the maximum allowable dwell time of the vehicle without affecting the operation task based on the remaining working time of the current task, return power consumption and driving power consumption, and define it as the network access delay interval;

[0031] S512: The duration of the network access delay interval is superimposed on the current time, the loading time of the load resonance segment on the time axis is postponed, the access time point after the time offset is calculated, and the time node for planning the charging access operation is used to obtain the suggested access time.

[0032] S513: Check whether the suggested access time falls within the time range of the corrected pre-charging window. When it is detected that the suggested access time is within the time range of the corrected pre-charging window, lock the suggested access time as the official start time of the charging operation, and generate a charging start command in combination with the target charging pile location information.

[0033] An IGV fleet intelligent charging route planning and power grid load balancing system, the system comprising:

[0034] The battery energy decay analysis module collects the battery voltage sequence and battery charge sequence of the on-board battery during the operation cycle of the IGV performing continuous work tasks, determines the reversal point of the battery energy decay trend, and measures the stable battery power supply time.

[0035] The pre-charge window prediction and correction module predicts the next decay change time based on the latest decay trend reversal point and the battery power supply stability duration, and constructs an initial pre-charge window. It calculates the expected terminal state value by combining the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging pile, and corrects the initial pre-charge window to obtain the corrected pre-charge window.

[0036] The power feature extraction module simulates the vehicle charging process based on the expected terminal state value and the preset standard charging control parameters, divides the charging power segment into multiple charging power segments, and extracts the power segment morphological feature set.

[0037] The resonance identification module collects the real-time total power load sequence at the regional power grid connection point, calculates the current power grid load ramp-up rate, compares it with the power segment morphology feature set, and marks the load resonance segment.

[0038] The charging instruction generation module obtains the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, defines the network access delay interval, and makes a joint judgment based on the load resonance segment and the corrected pre-charging window to generate a charging start instruction.

[0039] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0040] In this invention, the reversal point of energy decay trend is captured by collecting battery operation data, the stable power supply duration is quantified to accurately predict the moment of performance change, a pre-charging time window with safety redundancy is constructed, the power curve of the whole process is simulated based on charging parameters, the power climb pattern characteristics are extracted and compared with the real-time grid load climb rate, the risk period that may cause load resonance is identified, and the grid access delay interval is calculated by combining the vehicle's remaining tasks and return trip energy consumption. Under the premise of ensuring that the task connection is not affected, the charging access time is dynamically shifted, and the peak-shifting scheduling in the time dimension is used to eliminate the superposition effect of charging load and grid load. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the workflow of the present invention;

[0042] Figure 2 This is a flowchart of step S1 of the present invention;

[0043] Figure 3 This is a flowchart of step S2 of the present invention;

[0044] Figure 4 This is a flowchart of step S3 of the present invention;

[0045] Figure 5 This is a flowchart of step S4 of the present invention;

[0046] Figure 6 This is a flowchart of step S5 of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0048] Please see Figure 1 This invention provides a technical solution for intelligent charging route planning and grid load balancing for IGV fleets, comprising the following steps:

[0049] S1: Collect the battery voltage sequence and battery charge sequence of the on-board battery during the continuous operation cycle of the IGV to determine the reversal point of the battery energy decay trend and measure the stable battery power supply time.

[0050] S2: Based on the latest decay trend reversal point and the stable battery power supply duration, predict the next decay mutation time and construct an initial pre-charge window. Combine the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging station to calculate the expected terminal state value and correct the initial pre-charge window to obtain the corrected pre-charge window.

[0051] S3: Simulate the vehicle charging process based on the expected terminal status value and the preset standard charging control parameters, divide the charging power segment into multiple charging power segments, and extract the power segment morphological feature set;

[0052] S4: Collect the real-time total power load sequence at the grid connection point of the area, calculate the grid load ramp-up rate at the current moment, compare it with the power segment morphology feature set, and mark the load resonance segment;

[0053] S5: Obtain the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, define the network access delay interval, and make a joint judgment based on the load resonance segment and the corrected pre-charging window to generate a charging start command.

[0054] The battery power supply stability duration is specifically calculated by measuring the time span between two adjacent points where the degradation trend reverses. The corrected pre-charge window includes the window opening time starting from the current time and the window closing time determined by shifting forward based on the expected terminal state value. The power segment morphology feature set includes a sequence of independent charging power segments cut according to the inflection point of the power change rate and the power amplitude increase rate corresponding to the charging power segment. The load resonance segment includes a power segment identifier that is consistent with the direction of change of the grid load climbing rate and the absolute deviation between the power amplitude increase rate and the grid load climbing rate. The charging start command includes the locked charging start time that falls within the time range of the corrected pre-charge window and the target charging pile location code selected based on the principle of minimizing driving power consumption.

[0055] Please see Figure 2 The specific steps for obtaining the stable battery power duration are as follows:

[0056] S111: During the operation cycle of the IGV performing continuous operation tasks, the battery voltage sampling sequence and battery power sampling sequence of the vehicle battery are collected in real time according to the preset sampling frequency. The voltage drop amplitude of adjacent sampling time is calculated for the battery voltage sampling sequence, and the power difference of adjacent sampling time is calculated for the battery power sampling sequence. The battery energy decay rate is calculated based on the voltage drop amplitude and the power difference.

[0057] During the continuous operation cycle of an automated guided vehicle (AGV), voltage and current sensors integrated into the onboard battery management unit are used to discretize and collect terminal voltage and remaining charge data of the battery according to a pre-set sampling frequency, constructing separate battery voltage and charge sampling sequences. The sampling frequency is set based on the Nyquist sampling theorem, by statistically analyzing the signal frequency characteristics of the onboard battery under historical high-frequency load fluctuations, selecting a value at least twice the highest signal frequency as the sampling frequency. For example, if historical monitoring data shows that the cutoff frequency of the battery load fluctuation signal is 50 Hz, the sampling frequency is set to 120 Hz to ensure complete capture of voltage transient characteristics. For the battery voltage sampling sequence, the voltage value at the current sampling moment is subtracted from the voltage value at the previous sampling moment, and the absolute value is used to obtain the voltage drop amplitude between adjacent sampling moments. For the battery charge sampling sequence, the charge value at the previous sampling moment is subtracted from the charge value at the current sampling moment to obtain the charge difference between adjacent sampling moments. Subsequently, the voltage drop is used as the divisor, and the difference in battery capacity is used as the divisor to perform a ratio calculation, yielding the voltage drop per unit of energy consumption. This ratio is defined as the battery energy decay rate. Taking actual collected data as an example, if the voltage at a certain sampling moment is 48.50 volts and the previous moment was 48.55 volts, then the voltage drop is calculated as follows: Volts; the corresponding charge changes from 90.00 amp-hours to 89.90 amp-hours, and the charge difference is calculated as follows: Ah. Substituting the above values ​​into the calculation, the battery energy decay rate is... Volts / Actuates.

[0058] S112: Real-time monitoring of whether the battery energy decay rate is within the preset stable range. When the battery energy decay rate is detected to jump out of the stable range and suddenly change to the accelerated range, the moment of the sudden change is captured as the point of reversal of the decay trend.

[0059] The real-time calculated battery energy decay rate is input into the state monitoring logic, which has preset numerical ranges for a stable range and an acceleration range. The numerical boundaries of the stable and acceleration ranges are set based on the discharge characteristic curve in the battery's factory test data. This is achieved through statistical analysis of the discharge slope over the entire battery's lifespan, with the range determined by adding or subtracting one standard deviation from the mean slope of the flat region of the discharge curve as the stable range. For example, if the statistically obtained mean slope of the flat region is 0.45 and the standard deviation is 0.05, then the stable range is set to... The minimum slope of the steep voltage drop region at the end of the discharge curve is set as the starting boundary of the acceleration zone, for example, 0.60. During monitoring, the relationship between the current battery energy decay rate and the above-mentioned zone boundary is compared in real time. When the battery energy decay rate corresponding to a consecutive preset number of sampling points is greater than the upper boundary value of the stable zone, and simultaneously falls within the value range of the acceleration zone, the battery is determined to have entered a state of accelerated performance decay. For example, if the currently calculated decay rate is 0.65 volts / ampere-hour, this value satisfies... and If the condition is met, the system clock time at which the state determination is established is immediately locked, the timestamp data of that time is recorded, and it is marked as the point where the decay trend reverses.

[0060] S113: Arrange the decay trend reversal points in chronological order of occurrence, select two adjacent decay trend reversal points, extract the marker time corresponding to the latter decay trend reversal point and the marker time corresponding to the former decay trend reversal point respectively, calculate the time difference between the two marker times, and define the time difference as the battery power supply stable duration.

[0061] A first-in, first-out (FIFO) timestamp queue is maintained in the storage space to store captured decay trend reversal points in chronological order. When there are at least two records in the queue, the timestamp of the latest recorded decay trend reversal point and the timestamp of the previous decay trend reversal point immediately preceding it are retrieved. The time interval between the two state change points is obtained by subtracting the marked time of the previous decay trend reversal point from the marked time of the subsequent reversal point. This time interval represents the duration for which the battery can maintain a stable voltage output within a complete stable discharge cycle. The calculated time difference is defined as the battery power supply stability duration. Assuming the marked time of the previous decay trend reversal point is 10:30 and the marked time of the subsequent reversal point is 14:45, converting the time to minutes for calculation, the battery power supply stability duration is... minute.

[0062] Please see Figure 3 The corrected steps for obtaining the precharge window are as follows:

[0063] S211: Extract the time position corresponding to the latest decay trend reversal point, sum it with the battery power supply stable duration, predict the time when the next decay change will occur, extract the time period from the current time to the predicted time of the next decay change, define it as the time range of the initial planned charging task, and construct the initial pre-charging window.

[0064] The system reads the latest time position data corresponding to the reversal point of the degradation trend stored in the register, and reads the measured stable battery power supply duration. The latest reversal point time is added to the stable battery power supply duration, and the result is used as the predicted future time when the battery will enter an accelerated degradation state. Starting from the current system time and ending at the predicted next degradation abrupt change time, a continuous interval is extracted from the timeline. This time interval represents the window of time when battery performance is relatively stable and suitable for continued operation. This period is defined as the time range for the initial planned charging task, and the initial pre-charge window is constructed accordingly. For example, if the latest reversal point of the degradation trend is 14 hours and 45 minutes (i.e., 885 minutes), and the stable battery power supply duration is 255 minutes, then the predicted abrupt change time is... Minutes, i.e., 19:00. If the current time is 15:00, the initial precharge window is set to... .

[0065] S212: Obtain the current location coordinates of the IGV and the location coordinates of each idle charging pile, calculate the corresponding driving distance, obtain the vehicle's average energy consumption, convert the driving distance into the driving power consumption to the charging pile, calculate the voltage drop and power drop caused by the driving power consumption, calculate the difference between the sampled battery voltage and battery power at the current moment and the corresponding voltage drop and power drop, and obtain the expected terminal state value.

[0066] The system reads the global coordinates of the automated guided vehicle (AGV) at the current moment and retrieves the location coordinates of all idle charging piles from the map database. Using Euclidean distance calculation logic, it calculates the planned path distance between the vehicle's current coordinates and the coordinates of each idle charging pile, which is then used as the driving distance. To improve the accuracy of the driving distance calculation under actual working conditions, in scenarios with obstacles, fixed passages, restricted areas, one-way streets, and turning restrictions, it can also utilize the passable topology information stored in the map database. This involves calling path planning methods including grid map search, topology path search, and hybrid path search, comprehensively considering vehicle motion constraints, passage structure, turning radius, and obstacle avoidance requirements to search for a passable path from the vehicle's current location to the target charging pile, obtaining a non-linear path distance composed of multiple drivable segments. When there is a discrepancy between the passable path obtained by the path planning module and the Euclidean distance, the non-linear path distance, which includes turning arcs, passage polylines, and necessary detours, is prioritized as the vehicle's driving distance, making the calculation results more consistent with the actual driving conditions of the vehicle in warehouse or factory passageways. For example, if the straight-line distance from the vehicle's current location to the charging station is only 30 meters, but due to a shelf obstructing the way, it needs to travel 20 meters along the main aisle, turn right, and then travel 15 meters along a side aisle, resulting in a total path distance of 35 meters, then 35 meters is used as the travel distance for subsequent energy consumption calculations. The vehicle's historical energy consumption database is accessed to calculate the average energy consumption per unit mileage over a preset period. Multiplying the travel distance by the average energy consumption yields the required energy consumption to reach each charging station. Based on the voltage drop and charge reduction characteristics of the battery during discharge, the energy consumption is mapped to a certain magnitude of voltage and charge reduction to reflect the changes in remaining voltage and charge due to energy consumption before reaching the target charging station. The current real-time battery voltage and charge values ​​are read, and the calculated voltage and charge reduction values ​​are subtracted from them to obtain the expected remaining voltage and charge values ​​upon arrival at the charging station. This set of data is defined as the expected terminal state value.

[0067] S213: Compare the expected terminal state value with the state threshold corresponding to the preset decay trend reversal point. When the expected terminal state value is detected to be lower than the corresponding state threshold, it is determined that the current battery state cannot support the predicted sudden change time. The end time parameter of the initial pre-charge window is forward-shifted to obtain the corrected pre-charge window.

[0068] The system invokes a preset state threshold corresponding to the inversion point of the degradation trend. This threshold is set based on the undervoltage protection mechanism in the battery management logic, selecting the lowest allowable discharge cutoff voltage and the lowest retained charge percentage as critical standards. The voltage and charge components of the expected terminal state value are compared to the state threshold values. If the expected terminal state value is detected to be lower than the corresponding state threshold, it indicates that the current charge is insufficient. At this point, a comprehensive risk assessment algorithm calculates the necessary advance adjustment time. The specific calculation is based on the following formula:

[0069] ;

[0070] In the formula, This is the lead time, expressed in minutes. This is a risk redundancy factor. The value of this factor is set based on the battery's aging level; the higher the battery's internal resistance, the larger the factor should be to increase the safety margin. It is typically set within a certain range. to between; The cutoff voltage threshold is set according to the battery datasheet; The expected terminal voltage value is derived from the calculation results of S212; The average voltage discharge slope of the vehicle is obtained by fitting historical voltage drop curves. To retain the battery capacity threshold, it is set based on the minimum recharge requirement; This represents the estimated terminal battery level. The slope of the vehicle's average battery consumption is obtained by statistically analyzing the battery consumption per unit time. The function that takes the maximum value means comparing numerical values. Calculated difference The larger of the two values ​​is taken. If the calculated difference is negative (i.e., the expected value is higher than the threshold, meeting the requirements), the function result is: This indicates that this dimension does not contribute to the correction. If the calculated difference is positive (i.e., the predicted value is lower than the threshold, indicating a risk), the function result is the difference itself. In the formula... The logic ensures that a correction is only generated when the actual value is below a threshold (i.e., the difference is positive). The calculation yields... Then, subtract this value from the end time parameter of the initial precharge window to obtain the corrected precharge window. For example, set... threshold Volts, predicted value Volts, discharge slope Volts per minute; threshold Predicted value (At this point, the battery level is sufficient, and there is no risk of battery depletion), consumption slope / minute. Substitute into the calculation: the voltage term is The battery level item is... The square of is 0. Minutes. If the original end time was 1140 minutes, the corrected time is... minute.

[0071] Please see Figure 4 The specific steps for obtaining the power segment morphological feature set are as follows:

[0072] S311: The expected terminal state value is used as the starting power and voltage conditions for the simulated charging operation. Substitute it into the preset standard charging control parameters to simulate the operation process of the vehicle continuously charging from the current expected terminal state value until it is restored to a fully charged state, and construct the expected charging power curve.

[0073] The process of simulating a vehicle continuously charging from its current expected terminal state until it returns to a fully charged state is as follows:

[0074] The initial battery voltage data is obtained by analyzing the expected terminal state value, and then compared with the constant current to constant voltage threshold voltage in the standard charging control parameters.

[0075] When the initial battery voltage data is less than the constant current to constant voltage threshold voltage, the constant current stage simulation is started. The simulated charging current is set to the standard constant current value in the standard charging control parameters. The power accumulation is performed according to the preset time step and the simulated battery voltage is updated in real time until the simulated battery voltage rises to the constant current to constant voltage threshold voltage.

[0076] Then, the constant voltage stage simulation is started, and the simulated charging voltage is locked to the constant current to constant voltage threshold voltage. Based on the current decay logic defined in the standard charging control parameters, a simulated charging current that decreases over time is generated until the simulated charging current decreases to the preset charging cutoff current threshold.

[0077] For each time step covered by the constant current stage simulation and the constant voltage stage simulation, the product of the simulated charging voltage and the simulated charging current is calculated to obtain the instantaneous power value. All instantaneous power values ​​are then spliced ​​together according to the time series to construct the expected charging power curve.

[0078] The voltage value is extracted from the calculated expected terminal state value and used as the starting voltage for the simulated charging process. The energy value from the expected terminal state value is used as the starting energy value. Preset standard charging control parameters are loaded, including the constant current charging current value, the threshold voltage value for constant current to constant voltage transition, and the charging cutoff current threshold. First, the starting voltage is compared with the constant current to constant voltage threshold voltage. If the starting voltage is less than the threshold, the constant current simulation stage begins, and the charging current is set to the standard constant current value. Within each simulation time step, the charged energy is calculated using the integral of current over time, and the simulated battery voltage is updated according to the mapping table between battery open-circuit voltage and state of charge until the simulated voltage reaches the constant current to constant voltage threshold voltage. Then, the constant voltage simulation stage begins, fixing the simulated voltage to the threshold voltage. Based on the physical model for eliminating battery polarization effects, the simulated current decays exponentially over time. At each time step node of the simulation cycle, the product of the current simulated voltage and simulated current is calculated to obtain the instantaneous power value. The instantaneous power values ​​calculated for all time steps are connected in a time series to construct a complete expected charging power curve. For example, the initial voltage is 47.8 volts, the constant current to constant voltage threshold is 54 volts, and the standard constant current is 20 amperes. If the voltage rises to 50 volts at a certain moment during the constant current phase, then the instantaneous power at that moment is... watt.

[0079] S312: Calculate the rate of change of charging power over time in the expected charging power curve, monitor the alternation of positive and negative signs in the power change rate, locate the moment when the power change rate reverses from positive to negative and mark it as the inflection point, use all inflection points as the boundary for dividing the curve, and cut the expected charging power curve into multiple independent time-power data intervals to obtain charging power segments.

[0080] Discrete differential operations are performed on the constructed projected charging power curve. For each data point on the curve, the power difference between it and the next adjacent data point is calculated, and this difference is divided by the time step to obtain the charging power change rate at that moment. All calculated power change rate values ​​are scanned in chronological order, with a focus on monitoring the sign of the change rate values. When the sign of the power change rate changes from positive to negative, or from negative to positive, the position is determined to be a point of abrupt change in the shape characteristic of the curve and marked as an inflection point. The time coordinates of all marked inflection points are extracted and used as cutting lines to physically divide the continuous projected charging power curve into several independent time-power data segments on the time axis. Each segmented independent data segment is a charging power segment. For example, the power is 1000 watts at the 10th minute and 1010 watts at the 11th minute, with a change rate of... Watts per minute (positive). Power was 1200 watts in the 50th minute and 1190 watts in the 51st minute, with a change rate of... Watts per minute (negative sign). During this process, sign inversion is detected, inflection points are marked, and a cut is performed.

[0081] S313: For each charging power segment obtained by segmentation, extract the power amplitude corresponding to the start and end positions within the segment, calculate the power amplitude increase rate in combination with the time span covered by the segment, and summarize the power amplitude increase rate of each segment in chronological order to establish a power segment morphological feature set.

[0082] Iterate through each segment of charging power, reading the power values ​​at the start and end points of that segment on the time axis. Subtract the start point power from the end point power to obtain the power change; read the end point time and the start point time, and subtract them to obtain the time span. Divide the power change by the time span to calculate the power amplitude increase rate of that segment. This indicator quantifies the average rate and direction of change in charging power within the corresponding time period of the segment. Arrange and index the calculated power amplitude increase rates of all segments according to the chronological order of the corresponding segments in the original curve to establish a power segment morphological feature set. For example, a segment starts at the 10th minute with a power of 1000 watts and ends at the 20th minute with a power of 1200 watts. The power change is... Watt, time span Minutes. Power amplitude increase rate Watts per minute.

[0083] Please see Figure 5 The specific steps for obtaining the load resonance segment are as follows:

[0084] S411: Real-time monitoring and collection of the real-time total power load sequence at the regional power grid connection point. Based on the preset sliding time window duration, extract the current moment and a preset number of continuous sampling points from the real-time total power load sequence as calculation samples. Call the least squares method to perform linear fitting on the load within the calculation samples to obtain the power grid load ramp rate.

[0085] The total power values ​​at the regional power grid connection are collected in real time via the data interface of the regional microgrid, with a time resolution of seconds, forming a real-time total power load sequence. A fixed-length time sliding window is set, and all historical power sampling points within the length of the sliding window are extracted backward from the current time to form a calculation sample set. The least squares algorithm is used to perform linear fitting on the load within the calculation sample to obtain the grid load ramp-up rate. The calculation formula is as follows: In the formula, This represents the rate of increase in grid load, measured in watts per minute. The total number of sampling points in the sample set is determined by dividing the sliding window length by the sampling interval. Representing the Time coordinates of each sampling point; Represents the arithmetic mean of the time coordinates of all sampling points; Representing the Real-time power load values ​​at each sampling point; This represents the arithmetic mean of the power load at all sampling points; This is a regularization damping parameter used to prevent calculation overflow due to an excessively small denominator. Its value is set according to the order of magnitude of the time sampling precision; for example, taking... The slope parameter calculated using this formula quantifies the current trend of power grid load change over time. For example, three sample points were collected: time coordinates of 1, 2, and 3 minutes; corresponding load values ​​of 5000, 5020, and 5040 watts. The time mean was calculated. Average load Substitute into the numerator for calculation: Substitute the values ​​into the denominator to calculate: ,but Watts per minute.

[0086] S412: Extract the power amplitude increase rate corresponding to each charging power segment in the power segment morphology feature set one by one, compare the positive and negative signs of the grid load increase rate and each power amplitude increase rate to determine the consistency of the change direction, calculate the difference between the grid load increase rate and the power amplitude increase rate with the same change direction, and generate the rate deviation amplitude value.

[0087] The system sequentially reads the power amplitude rise rate value from the power segment morphology feature set. First, it executes sign comparison logic to determine if the sign of the currently read power amplitude rise rate matches the sign of the real-time calculated grid load ramp-up rate. If both signs are the same (both positive or both negative), it indicates that the direction of change in charging power and the direction of change in grid load have a superimposed and reinforcing trend, and are therefore determined to be consistent in direction. For data pairs with consistent directions, the absolute value of the difference between the grid load ramp-up rate and the power amplitude rise rate is calculated. This absolute value reflects the closeness of their change rates and is generated as the rate deviation amplitude value. For example, if the grid load ramp-up rate is 19.99 watts / minute (positive) and the power amplitude rise rate of a certain charging segment is 20 watts / minute (positive), the signs are consistent. The rate deviation amplitude value is... Watts per minute. If the rate of increase of another segment is 10,000 watts per minute, then the deviation amplitude is [value missing]. Watts per minute.

[0088] S413: Compare the rate deviation amplitude value with the preset matching threshold, filter the data entries whose rate deviation amplitude value is less than the preset matching threshold, trace the index position of the corresponding charging power segment in the original expected charging power curve, and establish the load resonance segment.

[0089] A preset matching threshold is invoked. This threshold is set based on the rated capacity margin of the grid transformer and is determined by calculating the maximum allowable instantaneous power fluctuation slope within the safe operating range of the transformer, for example, set to 5000 watts / minute. The calculated rate deviation amplitude value is compared numerically with this matching threshold. Data entries with rate deviation amplitude values ​​strictly less than the matching threshold are filtered out. For each filtered data entry, the index number of its corresponding power amplitude rise rate in the original feature set is read. Based on the index number, the specific time position of the charging power segment corresponding to that slope in the original expected charging power curve is traced back. These specific charging power segments are extracted and marked as risk segments that may cause grid load resonance, establishing a load resonance segment set. Taking the aforementioned data as an example, a deviation amplitude value of 9980.01 is greater than the threshold of 5000 and is not included; a deviation amplitude value of 0.01 is less than the threshold of 5000, and the charging segment corresponding to that rise rate is marked as a load resonance segment.

[0090] Please see Figure 6 The specific steps for obtaining the charging start command are as follows:

[0091] S511: Obtain vehicle operation task scheduling information, extract the remaining working time of the current task and the coordinates of the next work area, calculate the return distance from the charging pile to the next work area after charging is completed by combining the coordinates of the location of the idle charging pile, obtain the vehicle's average energy consumption parameters and convert the return distance into return power consumption, calculate the maximum allowable dwell time of the vehicle without affecting the operation task based on the remaining working time of the current task, return power consumption and driving power consumption, and define it as the network access delay interval;

[0092] The system obtains the currently executing task instructions from the vehicle dispatch system interface, analyzes the remaining workload and standard operation efficiency in the instruction packet, and divides them to calculate the remaining working time of the current task. It reads the geographical coordinates of the next work area, combines them with the coordinates of the selected idle charging pile, and calculates the path distance from the charging pile to the next work area, defining it as the return distance. It converts the return distance into return power consumption using the vehicle's average energy consumption parameter. It retrieves the total battery capacity data, subtracts the predicted power consumption corresponding to the remaining working time of the current task, the power consumption for traveling to the charging pile, and the return power consumption from the battery's maximum allowable discharge capacity. If the remaining power is still greater than the safety threshold, it divides the remaining power by the vehicle's average standby power. The result is the maximum time the vehicle is allowed to wait at the current location or charging pile without affecting subsequent task connections, i.e., the network access delay interval. For example, the battery's maximum discharge capacity is 100 amp-hours. The current task consumes 20 amp-hours, traveling to the charging pile consumes 5 amp-hours, and the return trip consumes 5 amp-hours. The safety threshold is 10 amp-hours. The remaining available power is... Ampere. If the standby current is 1 ampere, then the network access delay range is... Hour.

[0093] S512: The duration of the network access delay interval is added to the current time, the loading time of the load resonance segment on the time axis is postponed, the access time point after the time offset is calculated, and the time node for planning the charging access operation is used to obtain the suggested access time.

[0094] Read the current system time and the calculated duration of the grid connection delay interval. Read the original start time of the determined load resonance segment in the expected charging power curve. Add the duration of the grid connection delay interval to the current system time to construct an adjustable time offset. Apply this offset to the original time axis of the load resonance segment, shifting the high-power charging phase that would originally overlap with grid peaks backward on the time axis. Calculate the start time of the shifted charging curve; this time is the time node for the planned charging access operation after peak avoidance optimization, and output it as the suggested access time. For example, the load resonance segment is originally scheduled to start at the 10th minute. The current system time is 15:00. If the grid connection delay interval allows for an adjustment of 30 minutes, then the start time is postponed by 30 minutes. The suggested access time is calculated as follows: .

[0095] S513: Check whether the suggested access time falls within the time range of the corrected pre-charging window. When it is detected that the suggested access time is within the time range of the corrected pre-charging window, lock the suggested access time as the official start time of the charging operation, and generate a charging start command in combination with the target charging pile location information.

[0096] Read the start and end time parameters of the corrected pre-charge window. Determine the inclusion relationship between the calculated suggested access time and the time range of the window. Check if the suggested access time is greater than or equal to the start time of the corrected pre-charge window, and simultaneously less than or equal to the end time of the corrected pre-charge window. This determination process has two possibilities: If the determination result is true, it indicates that the suggested time avoids the risk of grid load resonance and meets the safety requirements of battery range. Therefore, the suggested access time is locked as the official start time of the charging operation, and a charging start command is generated based on the target charging pile location information. If the determination result is false, it indicates that delaying charging will lead to the risk of battery depletion. In this case, the peak avoidance strategy is abandoned, and the start time of the corrected pre-charge window is directly used as the charging start time. For example, the corrected pre-charge window is... The recommended access time is 15:30. Since 15:30 falls between 15:00 and 18:50, it is considered true, and a command to start charging at 15:30 is generated.

[0097] An IGV fleet intelligent charging route planning and grid load balancing system is used to execute the aforementioned IGV fleet intelligent charging route planning and grid load balancing method. The system includes:

[0098] The battery energy decay analysis module collects the battery voltage sequence and battery charge sequence of the on-board battery during the operation cycle of the IGV performing continuous work tasks, determines the reversal point of the battery energy decay trend, and measures the stable battery power supply time.

[0099] The pre-charge window prediction and correction module predicts the next abrupt change in degradation based on the latest degradation trend reversal point and the stable battery power supply duration, and constructs an initial pre-charge window. It calculates the expected terminal state value by combining the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging station, and corrects the initial pre-charge window to obtain the corrected pre-charge window.

[0100] The power feature extraction module simulates the vehicle charging process based on the expected terminal state value and the preset standard charging control parameters, divides the charging power segment into multiple charging power segments, and extracts the power segment morphological feature set.

[0101] The resonance identification module collects the real-time total power load sequence at the regional power grid connection point, calculates the current power grid load ramp-up rate, compares it with the power segment morphology feature set, and marks the load resonance segment.

[0102] The charging instruction generation module obtains the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, defines the network access delay interval, and makes a joint judgment based on the load resonance segment and the corrected pre-charging window to generate a charging start instruction.

[0103] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent charging route planning and grid load balancing for IGV fleets, characterized in that, Includes the following steps: S1: Collect the battery voltage sequence and battery charge sequence of the on-board battery during the continuous operation cycle of the IGV to determine the reversal point of the battery energy decay trend and measure the stable battery power supply time. S2: Based on the latest decay trend reversal point and the battery power supply stability duration, predict the next decay change time and construct an initial pre-charge window. Combine the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging pile to calculate the expected terminal state value and correct the initial pre-charge window to obtain the corrected pre-charge window. S3: Simulate the vehicle charging process based on the expected terminal state value and the preset standard charging control parameters, divide the charging power segment into multiple charging power segments, and extract the power segment morphological feature set; S4: Collect the real-time total power load sequence at the regional power grid connection point, calculate the current power grid load ramp-up rate, compare it with the power segment morphology feature set, and mark the load resonance segment; S5: Obtain the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, define the network access delay interval, and make a joint judgment based on the load resonance segment and the corrected pre-charge window to generate a charging start command; The specific steps for obtaining the load resonance segment are as follows: S411: Real-time monitoring and collection of the real-time total power load sequence at the regional power grid connection point. Based on the preset sliding time window duration, extract the current moment and a preset number of continuous sampling points from the real-time total power load sequence as calculation samples. Call the least squares method to perform linear fitting on the load within the calculation samples to obtain the power grid load ramp rate. S412: Extract the power amplitude increase rate corresponding to each charging power segment in the power segment morphological feature set one by one, compare the positive and negative signs of the grid load increase rate and each power amplitude increase rate to determine the consistency of the change direction, calculate the difference between the grid load increase rate and the power amplitude increase rate with the same change direction, and generate the rate deviation amplitude value. S413: Compare the rate deviation amplitude value with a preset matching threshold, filter data entries whose rate deviation amplitude value is less than the preset matching threshold, trace the index position of the corresponding charging power segment in the original expected charging power curve, and establish a load resonance segment. The specific steps for obtaining the charging start command are as follows: S511: Obtain vehicle operation task scheduling information, extract the remaining working time of the current task and the coordinates of the next work area, calculate the return distance from the charging pile to the next work area after charging is completed by combining the coordinates of the location of the idle charging pile, obtain the vehicle's average energy consumption parameters and convert the return distance into return power consumption, calculate the maximum allowable dwell time of the vehicle without affecting the operation task based on the remaining working time of the current task, return power consumption and driving power consumption, and define it as the network access delay interval; S512: The duration of the network access delay interval is superimposed on the current time, the loading time of the load resonance segment on the time axis is postponed, the access time point after the time offset is calculated, and the time node for planning the charging access operation is used to obtain the suggested access time. S513: Check whether the suggested access time falls within the time range of the corrected pre-charging window. When it is detected that the suggested access time is within the time range of the corrected pre-charging window, lock the suggested access time as the official start time of the charging operation, and generate a charging start command in combination with the target charging pile location information.

2. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 1, characterized in that, The battery power supply stability duration is specifically calculated by measuring the time span between two adjacent points where the decay trend reverses. The corrected pre-charge window includes the window opening time starting from the current time and the window closing time determined by shifting forward based on the expected terminal state value. The power segment morphology feature set includes a sequence of independent charging power segments cut according to the inflection point of the power change rate and the power amplitude increase rate corresponding to the charging power segment. The load resonance segment includes a power segment identifier that is consistent with the direction of change of the grid load climbing rate and the absolute deviation between the power amplitude increase rate and the grid load climbing rate. The charging start command includes a locked charging start time that falls within the time range of the corrected pre-charge window and a target charging pile location code selected based on the principle of minimizing driving power consumption.

3. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 1, characterized in that, The specific steps for obtaining the stable battery power supply duration are as follows: S111: During the operation cycle of the IGV performing continuous operation tasks, the battery voltage sampling sequence and battery power sampling sequence of the vehicle battery are collected in real time according to the preset sampling frequency. The voltage drop amplitude of adjacent sampling time is calculated for the battery voltage sampling sequence, and the power difference of adjacent sampling time is calculated for the battery power sampling sequence. The battery energy decay rate is calculated based on the voltage drop amplitude and the power difference. S112: Real-time monitoring of whether the battery energy decay rate is within a preset stable range. When the battery energy decay rate is detected to jump out of the stable range and suddenly change to the acceleration range, the moment of the sudden change is captured as the point of reversal of the decay trend. S113: Arrange the decay trend reversal points in chronological order of occurrence, select two adjacent decay trend reversal points, extract the marker time corresponding to the latter decay trend reversal point and the marker time corresponding to the former decay trend reversal point respectively, calculate the time difference between the two marker times, and define the time difference as the battery power supply stable duration.

4. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 3, characterized in that, The specific steps for obtaining the corrected precharge window are as follows: S211: Extract the time position corresponding to the latest attenuation trend reversal point, sum it with the battery power supply stability time, predict the time when the next attenuation change will occur, extract the time period from the current time to the predicted time of the next attenuation change, define it as the time range of the initial planned charging task, and construct the initial pre-charging window. S212: Obtain the current location coordinates of the IGV and the location coordinates of each idle charging pile, calculate the corresponding driving distance, obtain the vehicle's average energy consumption, convert the driving distance into the driving power consumption to the charging pile, calculate the voltage drop and power drop caused by the driving power consumption, calculate the difference between the battery voltage sampled at the current moment and the corresponding voltage drop, and the difference between the battery power sampled at the current moment and the corresponding power drop, respectively, to obtain the expected terminal state value; S213: Compare the expected terminal state value with the state threshold corresponding to the preset decay trend reversal point. When the expected terminal state value is detected to be lower than the corresponding state threshold, it is determined that the current battery state cannot support the predicted sudden change time. The end time parameter of the initial pre-charge window is forward-shifted to obtain the corrected pre-charge window.

5. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 4, characterized in that, The end time parameter of the initial precharge window is corrected by forward shifting, using the formula: ； in, Allowing for lead time, This is the risk redundancy coefficient. This is the cutoff voltage threshold. To predict the terminal voltage value, The average voltage discharge slope of the vehicle. To retain the battery level threshold, To estimate the terminal's power consumption, The slope of the vehicle's average battery consumption; After calculating the timing advance, subtract the timing advance from the end time parameter of the initial precharge window to obtain the corrected precharge window.

6. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 4, characterized in that, The specific steps for obtaining the power segment morphological feature set are as follows: S311: The expected terminal state value is used as the starting power and voltage conditions for the simulated charging operation. Substitute it into the preset standard charging control parameters to simulate the operation process of the vehicle continuously charging from the current expected terminal state value until it is restored to a fully charged state, and construct the expected charging power curve. S312: Calculate the rate of change of charging power over time in the expected charging power curve, monitor the alternation of positive and negative signs in the power change rate, locate the moment when the power change rate reverses from positive to negative and mark it as the inflection point, use all inflection points as the boundary limits for curve segmentation, and cut the expected charging power curve into multiple independent time-power data intervals to obtain charging power segments. S313: For each of the segmented charging power segments, extract the power amplitude corresponding to the start and end positions within the segment, calculate the power amplitude increase rate in combination with the time span covered by the segment, and summarize the power amplitude increase rate of each segment in chronological order to establish a power segment morphological feature set.

7. The IGV fleet intelligent charging route planning and grid load balancing method according to claim 6, characterized in that, The process of simulating a vehicle continuously charging from its current expected terminal state until it returns to a fully charged state is as follows: The initial battery voltage data is obtained by analyzing the expected terminal state value, and then compared with the constant current to constant voltage threshold voltage in the standard charging control parameters. When the initial battery voltage data is less than the constant current to constant voltage threshold voltage, the constant current stage simulation is started. The simulated charging current is set to the standard constant current value in the standard charging control parameters. The power accumulation is performed according to the preset time step and the simulated battery voltage is updated in real time until the simulated battery voltage rises to the constant current to constant voltage threshold voltage. Then, the constant voltage stage simulation is started, and the simulated charging voltage is locked to the constant current to constant voltage threshold voltage. Based on the current decay logic defined in the standard charging control parameters, a simulated charging current that decreases over time is generated until the simulated charging current decreases to the preset charging cutoff current threshold. For each time step covered by the constant current stage simulation and the constant voltage stage simulation, the product of the simulated charging voltage and the simulated charging current is calculated to obtain the instantaneous power value. All instantaneous power values ​​are then spliced ​​together according to the time series to construct the expected charging power curve.

8. An IGV fleet intelligent charging route planning and grid load balancing system, used to execute the IGV fleet intelligent charging route planning and grid load balancing method according to any one of claims 1-7, characterized in that, include: The battery energy decay analysis module collects the battery voltage sequence and battery charge sequence of the on-board battery during the operation cycle of the IGV performing continuous work tasks, determines the reversal point of the battery energy decay trend, and measures the stable battery power supply time. The pre-charge window prediction and correction module predicts the next decay change time based on the latest decay trend reversal point and the battery power supply stability duration, and constructs an initial pre-charge window. It calculates the expected terminal state value by combining the current on-board battery voltage and power of the IGV with the power consumption of driving to the charging pile, and corrects the initial pre-charge window to obtain the corrected pre-charge window. The power feature extraction module simulates the vehicle charging process based on the expected terminal state value and the preset standard charging control parameters, divides the charging power segment into multiple charging power segments, and extracts the power segment morphological feature set. The resonance identification module collects the real-time total power load sequence at the regional power grid connection point, calculates the current power grid load ramp-up rate, compares it with the power segment morphology feature set, and marks the load resonance segment. The charging instruction generation module obtains the remaining working time of the current task and the coordinates of the next work area from the vehicle operation task scheduling information, defines the network access delay interval, and makes a joint judgment based on the load resonance segment and the corrected pre-charging window to generate a charging start instruction.

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