New energy vehicle charging control method and system for intelligent parking lot

Abstract

The invention relates to the technical field of new energy vehicle supporting facilities and intelligent power grid load control, in particular to an intelligent parking lot new energy vehicle charging control method and system, and the method comprises the steps: collecting vehicle, time-space and power grid data; constructing a zero-relaxation ideal charging reference representing the minimum constant power and a dynamic available capacity envelope surface representing the maximum power boundary; calculating a real residual error reflecting demand deviation and a theoretical residual error reflecting system redundancy; based on the two types of residual errors, topological coupling judgment is carried out, and an optimal instruction is generated to adjust the charging power; according to the invention, the hidden danger of thermal runaway caused by sending a virtual high instruction to the battery in a low-temperature or high-charge state is avoided, and the environmental adaptability and safety of the system are greatly enhanced.

Description

Technical Field

[0001] This invention relates to the field of supporting facilities for new energy vehicles and the field of smart grid load control technology, specifically to a smart parking lot charging control method and system for new energy vehicles. Background Technology

[0002] In the current urban smart parking lot charging management environment, with the surge in the number of electric vehicles, charging facilities face multiple challenges such as high-concurrency access, limited grid capacity, and highly heterogeneous user demands; existing charging control solutions generally adopt a passive mode of first-come, first-served or simply responding to the power requests of the battery management system. While such solutions can meet basic charging needs under low-load scenarios, they face the following bottlenecks in actual operation: Due to the inability to accurately predict the actual departure time of vehicles and real-time grid load fluctuations, the system often blindly meets the initial aggressive power requests of vehicles, leading to severe overload risks for transformers during peak periods. Furthermore, vehicles urgently needing charging later cannot obtain power allocation due to capacity depletion. Traditional scheduling logic lacks an effective distinction between pseudo-rigid demands stemming from user anxiety and the physical limits of battery demand, resulting in the unnecessary occupation of instantaneous grid power resources and extremely uneven distribution of power resources in time and space. Existing systems typically fail to map and couple external grid power supply constraints with the internal battery pack's electrical physical characteristics in real time, causing a disconnect between command issuance and actual battery capacity under low temperature or high charge conditions. This reduces charging efficiency and increases the risk of battery thermal runaway. Therefore, how to effectively suppress pseudo-rigid demands, achieve flexible and optimized allocation of surplus grid capacity across multiple stations, and eliminate transformer overload hazards while ensuring all connected vehicles meet their power requirements upon departure, has become a pressing technical problem to solve. Summary of the Invention

[0003] The purpose of this invention is to provide an intelligent parking lot charging control method and system for new energy vehicles, which breaks through the traditional passive charging mode. While accurately ensuring that all vehicles meet the required charge level upon departure, it effectively suppresses pseudo-rigid demand and dynamically balances the grid load, thereby resolving the contradiction between transformer overload risk and charging efficiency and user experience. Specifically, the technical solution of this invention is as follows: A method for controlling the charging of new energy vehicles in an intelligent parking lot, comprising: Collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; Based on vehicle status data and spatiotemporal constraint data, a zero-relaxation ideal charging benchmark is constructed; the zero-relaxation ideal charging benchmark is the minimum constant power trajectory required for the vehicle to complete the preset charging task during the period from the current time to the departure time. Based on vehicle status data and power grid boundary data, a dynamic available capacity envelope is generated; the dynamic available capacity envelope is the maximum power boundary that the charging system can theoretically provide at the current moment after combining physical constraints and environmental disturbances. Based on vehicle status data, zero-relaxation ideal charging benchmark, and dynamic available capacity envelope, the actual residual and theoretical residual are calculated respectively. The actual residual represents the degree of deviation between the vehicle's nominal requested power and the baseline requirement, while the theoretical residual represents the system's surplus capacity after meeting the baseline requirement. Based on the actual residual and the theoretical residual, a topological coupling decision is made to generate the optimal charging control command, and the vehicle charging power is adjusted using the optimal charging control command.

[0004] Optionally, vehicle status data includes current state of charge, target state of charge, power requested by the battery management system, and battery temperature; The spatiotemporal constraint data includes vehicle entry time and user preset departure time; the power grid boundary data includes transformer rated capacity, transformer real-time total load, and non-charging facility basic load. The method for constructing a zero-relaxation ideal charging benchmark includes: Subtract the current state of charge from the target state of charge to obtain the energy gap value; Subtract the current system time from the user's preset departure time to obtain the remaining parking time; Multiply the energy deficit value by the battery capacity, and then divide by the remaining storage time to obtain the zero-slack ideal charging benchmark.

[0005] Optionally, methods for generating a dynamically available capacity envelope include: Based on the battery temperature and current state of charge, the physical charging capacity attenuation coefficient is obtained by querying the nonlinear polarization characteristic curve. Subtract the base load of non-charging facilities from the rated capacity of the transformer to obtain the net available capacity of the power grid. The physical charging capacity attenuation coefficient is used to calculate the total physical charging boundary at the vehicle end, and the net available capacity of the power grid is compared with the total physical charging boundary at the vehicle end. The smaller value is selected to generate the dynamic available capacity envelope.

[0006] Optionally, methods for calculating actual residuals and theoretical residuals separately include: Subtract the zero-relaxation ideal charging reference from the power requested by the battery management system, and mark the result as the actual residual; Calculate the zero-relaxation ideal charging benchmark for all vehicles in the current depot and obtain the total benchmark power. Subtract the sum of the reference power from the dynamic available capacity envelope, and label the result as the theoretical residual.

[0007] Optionally, methods for topological coupling decision-making based on actual residuals and theoretical residuals include: Construct a two-dimensional coupled decision space with the actual residual as the horizontal axis and the theoretical residual as the vertical axis; determine whether the theoretical residual is greater than zero; If the theoretical residual is less than or equal to zero, the system is determined to be in a rigid constraint state, and the optimal charging control command is set to be equal to or less than zero relaxed ideal charging reference. If the theoretical residual is greater than zero, the system is determined to be in an elastic allocation state. The projection weight of the actual residual in the system elastic space formed by the theoretical residual is calculated, and the theoretical residual is allocated to the corresponding vehicle according to the projection weight, generating the optimal charging control command that is higher than the zero-relaxed ideal charging reference.

[0008] Optionally, the method for generating the optimal charging control command also includes noise reduction logic: Set urgency thresholds and redundancy thresholds; If the actual residual of a vehicle is greater than the urgency threshold and the current theoretical residual is less than the redundancy threshold, then the power request of the vehicle's battery management system is determined to be pseudo-rigid demand noise. In response to noise identified as pseudo-rigid demand, the optimal charging control command is forcibly clamped to the zero-relaxation ideal charging reference to suppress the risk of grid overload.

[0009] Optionally, when the system is in a flexible allocation state, the methods for calculating the projection weights include: Obtain the actual residuals of all vehicles and mark the actual residuals that are greater than zero as positive urgency vectors; Calculate the sum of the values ​​of all positive urgency vectors and label it as the total urgency. Divide the actual residual of the target vehicle by the total urgency to obtain the projected weight of the vehicle; multiply the theoretical residual by the projected weight to obtain the elastic gain power. By adding the elastic gain power to the zero-relaxation ideal charging reference, the optimal charging control command is generated.

[0010] A smart parking lot new energy vehicle charging control system, used to implement the smart parking lot new energy vehicle charging control method as described in any one of claims 1-7, comprising: The data acquisition module is used to collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; The benchmark reconstruction module is used to construct a zero-relaxation ideal charging benchmark based on vehicle state data and spatiotemporal constraint data. The envelope generation module is used to generate a dynamic available capacity envelope based on vehicle status data and power grid boundary data. The residual extraction module is used to calculate the actual residual and the theoretical residual based on vehicle status data, zero-relaxation ideal charging reference and dynamic available capacity envelope. The coupling control module is used to make topological coupling decisions based on the actual residuals and theoretical residuals, generate the optimal charging control commands, and send them to the charging actuator.

[0011] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention constructs a zero-relaxation ideal charging benchmark, transforming discrete energy demand into a continuous minimum power trajectory, effectively eliminating user anxiety and aggressive demands in battery strategy; combined with real residual calculation, the system can accurately identify and suppress pseudo-rigid demand, prevent vehicles from unnecessarily occupying instantaneous power resources of the power grid, ensure that valuable capacity is reserved for vehicles with truly urgent needs, and significantly improve the spatiotemporal fairness of charging resource allocation. 2. This invention utilizes dynamic available capacity envelope technology to map the external power grid bottleneck and the internal battery pack's electrical physical characteristics to the same dimension. This envelope can dynamically expand and contract with changes in background load, which not only physically eliminates the risk of transformer overload tripping caused by blindly allocating power, but also avoids the risk of thermal runaway caused by sending false high-charge commands to the battery under low temperature or high charge conditions, greatly enhancing the system's environmental adaptability and safety. 3. This invention establishes a two-dimensional state-space coupled decision mechanism based on actual residuals and theoretical residuals, realizing flexible switching of control strategies; when power resources are scarce, rigid constraints are executed to ensure the basic needs of users, and when resources are abundant, projection weights are used for elastic allocation to improve the charging experience; this bidirectional coupled architecture effectively solves the contradiction between the risk of grid overload and charging efficiency in high-concurrency scenarios, and realizes the precise utilization of the grid's surplus capacity. 4. This invention adopts a modular system architecture, decoupling data perception, benchmark calculation and decision control, and is compatible with batteries of different brands and aging levels. By processing multi-source heterogeneous data such as vehicle status, spatiotemporal constraints and power grid boundaries in real time, the system has extremely high practical value and compatibility, and can flexibly adapt to the power environment of parking lots of various sizes such as commercial complexes or residential communities, solving the problem of single and rigid data processing in traditional dispatching systems. Attached Figure Description

[0012] The present invention will be further explained below with reference to the accompanying drawings and embodiments: Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a structural diagram of the system of the present invention. Detailed Implementation

[0013] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0014] Example 1: Please see Figure 1 A method for controlling the charging of new energy vehicles in an intelligent parking lot, comprising: Collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; Based on vehicle status data and spatiotemporal constraint data, a zero-relaxation ideal charging benchmark is constructed. The zero-relaxation ideal charging benchmark is the minimum constant power trajectory required for the vehicle to complete the preset charging task during the period from the current moment to the departure moment. Based on vehicle status data and grid boundary data, a dynamic available capacity envelope is generated; the dynamic available capacity envelope is the maximum power boundary that the charging system can theoretically provide at the current moment after combining physical constraints and environmental disturbances. Based on vehicle status data, zero-relaxation ideal charging benchmark, and dynamic available capacity envelope, the actual residual and theoretical residual are calculated respectively. The actual residual represents the degree of deviation between the vehicle's nominal requested power and the baseline requirement, while the theoretical residual represents the system's excess capacity after meeting the baseline requirement. Two-dimensional state-space coupling decision is performed based on the actual residual and the theoretical residual to generate the optimal charging control command, and the vehicle charging power is adjusted using the optimal charging control command.

[0015] This embodiment provides a smart parking lot charging control method for new energy vehicles. The core of this method is to break through the passive mode of traditional charging control that relies solely on first-come-first-served or BMS request response, and instead establish an active control architecture based on the bidirectional coupling of time and space bottom-line requirements and physical environment boundaries. The system acquires three types of core heterogeneous data through the data acquisition module, including the physical state of the vehicle's battery, the entry time obtained by the license plate recognition system, and the real-time load obtained by the transformer monitoring terminal. The system constructs a zero-relaxation ideal charging benchmark based on vehicle status data and spatiotemporal constraint data. This benchmark aims to construct a baseline power trajectory without redundancy or waste by spreading the vehicle's energy demand over the remaining parking time, thus eliminating users' psychological charging anxiety and aggressive requests from the BMS strategy. The system combines physical constraints and environmental disturbances to dynamically calculate the maximum power boundary of the charging system and generate a dynamic available capacity envelope. Based on this, the system performs dual-track differential calculations based on the above benchmark and envelope surface to obtain the real residuals that characterize the gap between what the vehicle wants and what the vehicle must have, and the theoretical residuals that characterize the gap between the system's supply capacity and the system's rigid expenditures. Based on the actual residual and the theoretical residual, a two-dimensional state space coupling decision is made, which projects the individual needs of the vehicle into the overall system capability space, and generates the optimal charging control command to adjust the output power of the charging pile. This embodiment transforms the charging control problem into a coupled optimization problem in residual space by introducing a zero-relaxation ideal charging reference and a dynamic available capacity envelope. In the high-concurrency scenario of smart parking lots, this bidirectional coupled architecture can accurately identify and suppress pseudo-rigid demand. Under the premise of ensuring that all users' departure power meets the standard, it makes full use of the surplus capacity of the power grid for flexible allocation, effectively solving the contradiction between transformer overload risk and charging efficiency.

[0016] Example 2: Vehicle status data includes current state of charge, target state of charge, power requested by the battery management system, and battery temperature; Spatiotemporal constraint data includes vehicle entry time and user-preset exit time; Grid boundary data includes transformer rated capacity, real-time total transformer load, and non-charging facility base load; Methods for constructing a zero-relaxation ideal charging benchmark include: Subtract the current state of charge from the target state of charge to obtain the energy gap value; Subtract the current system time from the user's preset departure time to obtain the remaining parking time; Multiply the energy deficit value by the battery capacity, and then divide by the remaining storage time to obtain the zero-slack ideal charging benchmark.

[0017] This embodiment is a further detail of the data composition and benchmark reconstruction process in Embodiment 1; Clearly define the specific definitions and physical sources of the input data: Vehicle status data is obtained through CAN bus communication between the charging pile and the BMS, where the battery temperature comes from the thermal management sensor of the BMS; the user-preset departure time in the spatiotemporal constraint data comes from the user's mobile terminal APP setting; the real-time total load of the transformer in the power grid boundary data comes from high-frequency sampling of the smart meter. To quantify the vehicle's minimum requirements, this embodiment uses the following calculation model to construct a zero-relaxation ideal charging benchmark. : The definitions and units of each parameter are as follows: : Derived from user settings or system default values, the physical meaning is the target state of charge, and the value is a decimal between 0 and 1. If the original data is in percentage format, it needs to be divided by 100 for normalization. : It originates from real-time reporting by BMS, and its physical meaning is the current state of charge, with a value between 0 and 1. : This data is derived from BMS static data reading and its physical meaning is the battery's rated capacity, expressed in kilowatt-hours (kWh). : Derived from user app settings or historical behavior predictions, the physical meaning is the user's preset departure time, which needs to be converted into a time value in hours (h) during calculation; : Derived from the system clock, its physical meaning is the current system time, and it needs to be converted into a time value in hours (h) during calculation; Derived from the BMS thermal management sensor, its physical meaning is battery temperature, and the unit is degrees Celsius (°C). It serves as the key physical input for subsequent nonlinear polarization characteristic curve lookup. The calculation process subtracts the current state of charge from the target state of charge to obtain the energy deficit value, and subtracts the current system time from the user's preset departure time to obtain the remaining parking time; Considering the risk of division by zero that may occur in real-world scenarios, the system has a built-in minimum time slice constraint logic. If the calculated remaining parking time is less than a preset threshold, such as 0.05 hours, it will be forcibly set to that threshold to prevent calculation overflow. In order to prevent the calculated zero-relaxation ideal charging benchmark from exceeding the physical limit due to an excessively small denominator, the system compares the calculation result with the maximum charging power allowed by the battery and takes the smaller value as the final benchmark value. Meanwhile, to avoid a negative power reference due to the user-set target state of charge being lower than the current state of charge, the system includes non-negative constraint logic: if the calculated energy gap value is less than or equal to zero, the ideal charging reference is forced to be zero-relaxed. Set to 0kW to ensure the correctness of the physical meaning; Multiply the energy deficit value by the battery capacity and divide by the remaining storage time to obtain the zero-relaxation ideal charging reference in kilowatts (kW). This embodiment transforms discrete energy demand into a continuous power reference using the above formula; in scenarios where vehicles are parked for extended periods... By eliminating nonlinear fluctuations caused by battery chemistry, such as artificially high requests before the end of the trickle charge, an absolutely objective physical reference is provided for subsequent power allocation, ensuring that vehicles do not unnecessarily occupy valuable instantaneous power resources of the power grid.

[0018] Example 3: Methods for generating a dynamically available capacity envelope include: Based on the battery temperature and current state of charge, the physical charging capacity attenuation coefficient is obtained by querying the nonlinear polarization characteristic curve. Subtract the base load of non-charging facilities from the rated capacity of the transformer to obtain the net available capacity of the power grid. The physical charging capacity attenuation coefficient is used to calculate the total physical charging boundary at the vehicle end, and the net available capacity of the power grid is compared with the total physical charging boundary at the vehicle end. The smaller value is selected to generate the dynamic available capacity envelope.

[0019] This embodiment is a further specification of the dynamic available capacity envelope generation logic in Embodiment 2; the process aims to determine the physical limits of the system at the current moment. The system queries a preset nonlinear polarization characteristic curve based on the battery temperature and current state of charge to obtain the physical charging capacity decay coefficient. This coefficient comes from a battery characteristic database calibrated in the laboratory. Its physical meaning is a dimensionless factor between 0 and 1, used to characterize the safety limits of a battery when it is charged with a large current at a specific temperature or high SOC state. Subtract the base load of non-charging facilities and other currently unschedulable high-priority loads from the rated capacity of the transformer to obtain the net available capacity of the power grid. The non-charging facility base load comes from the real-time monitoring of background loads such as lighting, HVAC, etc. by smart meters or building energy management system interfaces deployed on the incoming side of low-voltage distribution cabinets. The system calculates the physical charging capacity attenuation coefficient of all currently connected vehicles. ,in Indicates the first a car, This indicates the total number of electric vehicles currently connected to the charging system in the parking lot, combined with the rated power of each charging pile. Calculate the total physical charging boundary at the vehicle end; The system compares the net available capacity of the power grid with the total physical charging boundary at the vehicle end, and selects the smaller value as the dynamic available capacity envelope. The specific calculation model is defined as the following short-board constraint formula: In this formula, This represents the power supply bottleneck on the grid side. Represents the bottleneck of power reception on the battery side; physical charging capacity attenuation coefficient. As a safety weighting factor for each vehicle, the maximum allowable power of a single vehicle is limited; The physical significance of this step is to map the power supply capacity constraint of the external power grid and the power receiving capacity constraint of the internal battery pack to the same dimension, and take the intersection to ensure that the system not only does not overload, but also does not send false high-level commands to the batteries that exceed their physical capacity. In this formula, the net available capacity of the power grid As a reference amplitude, the physical charging capacity attenuation coefficient As a safety weighting factor; The physical significance of this step lies in mapping the power supply capacity constraint of the external power grid and the power receiving capacity constraint of the internal battery to the same dimension; for example, when the battery is in a low-temperature state, leading to... When the value drops to 0.8, even if the grid has 100kW of spare capacity, the system output envelope will be clamped at 80kW, thus generating a dynamic available capacity envelope. ; The dynamic available capacity envelope generated in this embodiment has significant environmental adaptability. In the complex power environment of shopping malls or residential communities, the envelope can dynamically expand and contract with changes in background loads such as elevators and air conditioners, just like a breathing boundary. This eliminates the risk of transformer tripping caused by blindly allocating power from a physical perspective, and also avoids the risk of battery thermal runaway under low temperature or high SOC conditions. To meet the requirement of code-level reproducibility, the digital implementation of the nonlinear polarization characteristic curve in the controller is explained in detail: A two-dimensional lookup table matrix is ​​constructed internally, with the row index being the battery temperature. Discretization interval is With a step size of 5°C, when the collected battery temperature exceeds this discretization range, the system executes a safety protection strategy: if the temperature is higher than 60°C, the physical charging capacity attenuation coefficient α is forcibly set to 0 to cut off the charging circuit and prevent thermal runaway. The column index is the current state of charge. Discretization interval is Step size 0.1; The table lookup logic follows these boundary rules: Low temperature cutoff: when At that time, the corresponding element of the matrix The value is forcibly set to The lower values ​​within the range are used to prevent lithium plating. End-of-line protection: when When using a linear cutoff strategy for calculation The value is calculated using the following formula: To ensure that the battery is fully charged At that time, the physical charging capacity coefficient is forcibly reduced to zero; Interpolation calculation: For input values ​​located in the middle of discrete nodes, the system uses bilinear interpolation to calculate the final value. coefficient; It should be noted that this embodiment directly uses the rated capacity of the transformer as the minuend in order to establish the absolute safety boundary of the system and ensure that the calculated net available capacity truly reflects the maximum surplus power that the power grid can provide without causing transformer overload. This is because to calculate the net available capacity, the base load must be subtracted from the total capacity limit; if the current actual operating load reading of the transformer is used directly, the remaining power supply capacity of the power grid cannot be accurately represented; the further explanation of the definition of this parameter in this specification is intended to ensure the physical feasibility and logical consistency of the technical solution.

[0020] Example 4: Methods for calculating actual residuals and theoretical residuals separately include: Subtract the zero-relaxation ideal charging reference from the power requested by the battery management system, and mark the result as the actual residual; Calculate the zero-relaxation ideal charging benchmark for all vehicles in the current depot and obtain the total benchmark power. Subtract the sum of the reference power from the dynamic available capacity envelope, and label the result as the theoretical residual.

[0021] This embodiment is a further specification of the residual calculation method in Embodiment 3. These two indicators are the bridge connecting the physical world and the control logic. For each individual vehicle, the system subtracts the zero-relaxation ideal charging reference from the battery management system's requested power and marks the result as the actual residual. The actual residual comes from the difference between the BMS real-time request and the system calculation benchmark, and its physical meaning represents the degree to which the vehicle is in a rapid charging state. The system calculates the zero-relaxation ideal charging benchmark for all vehicles in the current depot and obtains the total benchmark power. Subtract the reference total power from the dynamic available capacity envelope, and label the result as the theoretical residual. The theoretical residual originates from the difference between the system's maximum supply capacity and the system's minimum rigid expenditure, and its physical meaning characterizes the system's elasticity space. This embodiment uses dual-track differential extraction to decouple the complex scheduling problem into two evaluation dimensions: greediness at the individual level and redundancy at the system level. In scenarios with multiple vehicles charging concurrently, this quantitative method can clearly define the boundaries between what is desired and what is necessary, as well as the boundaries between what can be given and what must be given, providing a solid quantitative basis for subsequent accurate decision-making.

[0022] Example 5: Methods for two-dimensional state-space coupled decision-making based on actual residuals and theoretical residuals include: Construct a two-dimensional coupled decision space with the actual residual as the horizontal axis and the theoretical residual as the vertical axis; determine whether the theoretical residual is greater than zero; If the theoretical residual is less than or equal to zero, the system is determined to be in a rigid constraint state, and the optimal charging control command is set to be equal to or less than zero relaxed ideal charging reference. If the theoretical residual is greater than zero, the system is determined to be in an elastic allocation state. The projection weight of the actual residual in the system elastic space formed by the theoretical residual is calculated, and the theoretical residual is allocated to the corresponding vehicle according to the projection weight, generating the optimal charging control command that is higher than the zero-relaxed ideal charging reference.

[0023] This embodiment is a further specification of the two-dimensional state-space coupled decision logic in Embodiment 4; The system constructs a two-dimensional coupled decision space with the actual residual as the horizontal axis and the theoretical residual as the vertical axis, and maps the current state of all vehicles into this space; The system determines whether the theoretical residual is greater than zero; When the theoretical residual is less than or equal to zero, the system determines that it is in a rigid constraint state. At this time, the grid capacity is insufficient to support additional allocation. The system sets the optimal charging control command to be equal to or less than zero relaxed ideal charging reference. If necessary, a derating factor is introduced to prevent overload. The specific derating factor calculation and command generation logic are as follows: The system calculates the global depreciation factor. The calculation formula is: in, The dynamic available capacity envelope calculated in Example 3 is the envelope surface. For all currently connected vehicles, index From 1 to The sum of the zero-relaxation ideal charging references; The system uses this coefficient to compress the baseline for each vehicle and generate optimal charging control commands. : This logic ensures that when the theoretical residual... Even if the grid capacity cannot meet the minimum needs of all vehicles, the system can still adjust its response based on the urgency of each vehicle's needs. The size is reduced proportionally and fairly, thereby strictly clamping the total power within the safety boundary. Within; In response to the theoretical residual being greater than zero, the system determines that it is in an elastic allocation state. At this time, the power grid has surplus capacity. The system calculates the projection weight of the actual residual in the system elastic space formed by the theoretical residual, and intelligently allocates the surplus capacity to vehicles in need according to the weight, generating the optimal charging control command that is higher than the zero relaxation ideal charging benchmark. The coupled decision mechanism in this embodiment enables dynamic switching of control strategies. In actual operation with fluctuating power resources, this mechanism adopts egalitarianism to ensure a bottom line when resources are scarce, and on-demand allocation to improve the experience when resources are abundant. This effectively avoids the rigidity problem of traditional fixed priority strategies when resources fluctuate, and improves the service quality of charging facilities.

[0024] Example 6: The method for generating optimal charging control commands also includes noise reduction logic: Set urgency thresholds and redundancy thresholds; If the actual residual of a vehicle is greater than the urgency threshold and the current theoretical residual is less than the redundancy threshold, it indicates that the current grid capacity is insufficient to support the aggressive request. The system marks the request as pseudo-rigid demand noise to be suppressed according to the congestion management strategy. In response to noise identified as pseudo-rigid demand, the optimal charging control command is forcibly clamped to the zero-relaxation ideal charging reference to suppress the risk of grid overload.

[0025] This embodiment is a further specification of the instruction generation logic in embodiment 5, and introduces key denoising logic to identify pseudo rigid requirements; The system sets urgency thresholds and redundancy thresholds, which are derived from statistical analysis of historical operating data and are used to define abnormally high power requests and grid stress conditions, respectively. The system monitors the status of each vehicle in real time. If the actual residual of a vehicle is greater than the urgency threshold and the current theoretical residual is less than the redundancy threshold, the system determines that the power request of the vehicle's battery management system is pseudo-rigid demand noise. This noise usually corresponds to the situation where the vehicle is parked for a long time but the BMS still requests high power. In response to noise identified as pseudo-rigid demand, the system forcibly clamps the optimal charging control command to the zero-relaxation ideal charging reference, ignoring the high power request of its BMS. This embodiment effectively prevents vehicles that want to charge quickly but are not in a hurry from occupying resources when grid resources are scarce through noise reduction logic. In peak charging scenarios, this logic reserves valuable grid capacity for vehicles that are really about to leave and have insufficient power, reflecting fairness based on time and space value and significantly reducing the risk of grid overload. To ensure the feasibility of the algorithm parameters, the specific method for obtaining the threshold quantization is as follows: Urgency threshold The threshold is set to 30% of the rated power of a single charging pile. For example, for a 60kW standard DC charging pile, this threshold is initialized to 18kW. The logic behind this value is that when the vehicle's requested power exceeds its bottom-line requirement, that is, when the actual residual reaches this level, it indicates that the BMS is trying to initiate an aggressive fast charging request. Redundancy threshold : Set to 5% of the total transformer capacity. For example, for a 1000kVA transformer, this threshold is initialized to 50kW. This value defines the quasi-congestion zone of the system. That is, when the theoretical remaining capacity of the system is lower than this value, it is considered that the physical conditions for supporting aggressive fast charging are no longer met. Based on the above quantitative definition, the program can accurately execute the comparison logic: If AND If this occurs, a clamping operation will be triggered.

[0026] Example 7: When the system is in a flexible allocation state, the methods for calculating the projection weights include: Obtain the actual residuals of all vehicles and mark the actual residuals that are greater than zero as positive urgency vectors; calculate the sum of the values ​​of all positive urgency vectors and mark it as the total urgency. Divide the actual residual of the target vehicle by the total urgency to obtain the projected weight of the vehicle; multiply the theoretical residual by the projected weight to obtain the elastic gain power. By adding the elastic gain power to the zero-relaxation ideal charging reference, the optimal charging control command is generated.

[0027] This embodiment is a further specification of the calculation of projection weights under the flexible allocation state in Embodiment 5; The system obtains the actual residuals of all vehicles and marks the actual residuals that are greater than zero as positive urgency vectors. This means that only vehicles whose requested power is higher than the baseline power are eligible to participate in the allocation. The system calculates the sum of the values ​​of all positive urgency vectors and marks it as the total urgency. The unit of this parameter is kilowatts (kW); to ensure the rigor of the mathematical description, let the set be... For all satisfied Vehicle Index If the set is given, then the formula for calculating the total urgency is: Let the target vehicle have an index of . Divide the actual residual by the total urgency to obtain the projected weight of the vehicle. This parameter is a dimensionless value (0-1). It should be noted that, in order to conform to the logic of generating higher-than-baseline instructions in Example 5, for vehicles with actual residuals less than or equal to zero, the system forcibly sets their projection weight to 0, does not participate in the allocation of surplus capacity, and only operates at the baseline power. In this step, to meet the logical closed-loop requirements of the algorithm and prevent program crashes, the system introduces a non-zero denominator check logic: If the calculated total urgency If the projected weights are zero, meaning the actual residuals of all vehicles are less than or equal to zero, then no vehicle has an additional urgent need, and the projected weights of all vehicles are forced to be zero. Set to 0; unless the theoretical residual is at this point. If the value is greater than zero, the system enters the balanced fast charging mode: the projected weights are adjusted. Redefining it as the proportion of the vehicle's battery capacity to the total battery capacity connected to the depot, i.e. This allows the surplus capacity of the power grid to be allocated to all vehicles that are not fully charged according to their battery specifications. Conversely, division is performed. ; To prevent numerical instability caused by the precision of floating-point calculations, projection weights are used. The calculation introduces the minimum value ,For example Perform regularization: in, To prevent division by zero errors, the minimum regularization parameter has physical units equal to... Keep it consistent and set it to the kilowatt level, for example. ; This not only avoids division by zero errors, but also... The function reaffirms the logic of assigning only to the positive urgency vector; Based on this, the system will use the theoretical residual Multiply by the projection weight to obtain the vehicle's elastic gain power. Unit: kW, Calculation formula is: ; The zero-relaxation ideal charging benchmark corresponding to this vehicle By adding elastic gain power, optimal charging control commands are generated. Unit: kW ; To ensure charging safety and prevent commands from exceeding limits, the final optimal charging control command is generated. Finally, the system also includes an output saturation stage: The system will obtain the maximum charging power allowed by the vehicle's BMS. and the rated power of the charging pile The minimum value among the three is taken as the final instruction issued, that is: The power margin generated by the system's calculation instruction truncation ;like Greater than the preset allocation threshold, for example The system will execute a secondary allocation loop, which will... According to the above projection weights Reassigned to those not yet reached or The vehicles are restricted until the power margin is less than the preset minimum resolution, such as 0.1kW, or all vehicles reach the physical limit. in, Specifically, it refers to the maximum allowable charging power or current value sent in real time by the vehicle's battery management system (BMS) via CAN bus messages, such as the BCS message defined in the GB / T27930 standard. This value already includes the internal protection limits of the battery cells for temperature, voltage, and lithium plating risks. This step effectively prevents the risk of instructions exceeding the physical limits of the hardware due to excessive theoretically calculated elastic gains, ensuring the integrity of the technical solution. This embodiment achieves a non-linear power redistribution through a projection weighting mechanism. Unlike simple average distribution, this mechanism ensures that surplus capacity is prioritized for vehicles whose BMS requests are most different from the baseline, i.e., vehicles that truly need fast charging. At the same time, since the final instruction is based on a zero-slack ideal charging benchmark, even if no gain is allocated, the vehicle can still be fully charged on time, achieving a perfect balance between minimum requirements and acceleration experience.

[0028] Example 8: Please see Figure 2 A smart parking lot charging control system for new energy vehicles includes: The data acquisition module is used to collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; The benchmark reconstruction module is used to construct a zero-relaxation ideal charging benchmark based on vehicle state data and spatiotemporal constraint data. The envelope generation module is used to generate a dynamic available capacity envelope based on vehicle status data and power grid boundary data. The residual extraction module is used to calculate the actual residual and the theoretical residual based on vehicle status data, zero-relaxation ideal charging reference and dynamic available capacity envelope. The coupling control module is used to make two-dimensional state-space coupling decisions based on the actual residuals and theoretical residuals, generate the optimal charging control command, and send it to the charging actuator.

[0029] This embodiment provides an intelligent parking lot charging control system for new energy vehicles. This system is used to execute the methods described in any one of embodiments 1 to 7 above. The system's hardware architecture integrates a CAN communication unit embedded in the charging pile, a license plate recognition camera, a smart meter, and a core controller. Specifically, its functional modules include: The data acquisition module is configured to clean and synchronize multi-source heterogeneous data in real time, and is used to collect vehicle status data, spatiotemporal constraint data, and power grid boundary data. The benchmark reconstruction module is configured to be based on an embedded computing unit and is used to calculate the zero-relaxation ideal charging benchmark for each vehicle in real time based on vehicle status data and spatiotemporal constraint data. The envelope generation module is configured to combine the battery polarization curve library with the power grid load prediction model to generate a dynamic available capacity envelope based on vehicle status data and power grid boundary data. The residual extraction module is configured to execute differential calculation logic to calculate the actual residual and the theoretical residual based on vehicle status data, zero-relaxation ideal charging reference and dynamic available capacity envelope. The coupling control module is configured to execute two-dimensional state space decision, denoising and weight allocation logic. It is used to perform two-dimensional state space coupling decision based on the actual residual and the theoretical residual, generate the optimal charging control command, and send it to the charging actuator. This embodiment adopts a modular design, decoupling data perception, benchmark calculation, and decision control; in particular, the independent setting of the benchmark reconstruction module enables the system to be compatible with batteries of different brands and aging levels; in actual engineering deployment, this system architecture has extremely high practical value and compatibility, and can flexibly adapt to the power environment of parking lots of various sizes.

[0030] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for controlling the charging of new energy vehicles in an intelligent parking lot, characterized in that, include: Collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; Based on vehicle status data and spatiotemporal constraint data, a zero-relaxation ideal charging benchmark is constructed. The zero-relaxation ideal charging benchmark is the minimum constant power trajectory required for the vehicle to complete the preset charging task during the period from the current moment to the departure moment. Based on vehicle status data and power grid boundary data, a dynamic available capacity envelope is generated; the dynamic available capacity envelope is the maximum power boundary that the charging system can theoretically provide at the current moment after combining physical constraints and environmental disturbances. Based on vehicle status data, zero-relaxation ideal charging benchmark, and dynamic available capacity envelope, the actual residual and theoretical residual are calculated respectively. The actual residual represents the degree of deviation between the vehicle's nominal requested power and the baseline requirement, while the theoretical residual represents the system's surplus capacity after meeting the baseline requirement. Based on the actual residual and the theoretical residual, a topological coupling decision is made to generate the optimal charging control command, and the vehicle charging power is adjusted using the optimal charging control command.

2. The intelligent parking lot charging control method for new energy vehicles according to claim 1, characterized in that, The vehicle status data includes the current state of charge, target state of charge, power requested by the battery management system, and battery temperature. The spatiotemporal constraint data includes the vehicle entry time and the user's preset departure time; The power grid boundary data includes the transformer rated capacity, the real-time total load of the transformer, and the basic load of non-charging facilities. The method for constructing a zero-relaxation ideal charging benchmark includes: Subtract the current state of charge from the target state of charge to obtain the energy gap value; Subtract the current system time from the user's preset departure time to obtain the remaining parking time; Multiply the energy deficit value by the battery capacity, and then divide by the remaining storage time to obtain the zero-slack ideal charging benchmark.

3. The intelligent parking lot charging control method for new energy vehicles according to claim 2, characterized in that, The method for generating the dynamically available capacity envelope includes: Based on the battery temperature and current state of charge, the physical charging capacity attenuation coefficient is obtained by querying the nonlinear polarization characteristic curve. Subtract the base load of non-charging facilities from the rated capacity of the transformer to obtain the net available capacity of the power grid. The physical charging capacity attenuation coefficient is used to calculate the total physical charging boundary at the vehicle end, and the net available capacity of the power grid is compared with the total physical charging boundary at the vehicle end. The smaller value is selected to generate the dynamic available capacity envelope.

4. The intelligent parking lot charging control method for new energy vehicles according to claim 3, characterized in that, The methods for calculating the actual residuals and theoretical residuals separately include: Subtract the zero-relaxation ideal charging reference from the power requested by the battery management system, and mark the result as the actual residual; Calculate the zero-relaxation ideal charging benchmark for all vehicles in the current depot and obtain the total benchmark power. Subtract the sum of the reference power from the dynamic available capacity envelope, and label the result as the theoretical residual.

5. The intelligent parking lot charging control method for new energy vehicles according to claim 4, characterized in that, The method for topological coupling decision based on actual residuals and theoretical residuals includes: Construct a two-dimensional coupled decision space with the actual residual as the horizontal axis and the theoretical residual as the vertical axis; determine whether the theoretical residual is greater than zero; If the theoretical residual is less than or equal to zero, the system is determined to be in a rigid constraint state, and the optimal charging control command is set to be equal to or less than zero relaxed ideal charging reference. If the theoretical residual is greater than zero, the system is determined to be in an elastic allocation state. The projection weight of the actual residual in the system elastic space formed by the theoretical residual is calculated, and the theoretical residual is allocated to the corresponding vehicle according to the projection weight, generating the optimal charging control command that is higher than the zero-relaxed ideal charging reference.

6. The intelligent parking lot charging control method for new energy vehicles according to claim 5, characterized in that, The method for generating optimal charging control commands also includes noise reduction logic: Set urgency thresholds and redundancy thresholds; If the actual residual of a vehicle is greater than the urgency threshold and the current theoretical residual is less than the redundancy threshold, then the power request of the vehicle's battery management system is determined to be pseudo-rigid demand noise. In response to noise identified as pseudo-rigid demand, the optimal charging control command is forcibly clamped to the zero-relaxation ideal charging reference to suppress the risk of grid overload.

7. The intelligent parking lot charging control method for new energy vehicles according to claim 5, characterized in that, When the system is in a flexible allocation state, the method for calculating the projection weights includes: Obtain the actual residuals of all vehicles and mark the actual residuals that are greater than zero as positive urgency vectors; Calculate the sum of the values ​​of all positive urgency vectors and label it as the total urgency. Divide the actual residual of the target vehicle by the total urgency to obtain the projected weight of the vehicle; multiply the theoretical residual by the projected weight to obtain the elastic gain power. By adding the elastic gain power to the zero-relaxation ideal charging reference, the optimal charging control command is generated.

8. A smart parking lot new energy vehicle charging control system, used to implement the smart parking lot new energy vehicle charging control method as described in any one of claims 1-7, characterized in that, include: The data acquisition module is used to collect vehicle status data, spatiotemporal constraint data, and power grid boundary data; The benchmark reconstruction module is used to construct a zero-relaxation ideal charging benchmark based on vehicle state data and spatiotemporal constraint data. The envelope generation module is used to generate a dynamic available capacity envelope based on vehicle status data and power grid boundary data. The residual extraction module is used to calculate the actual residual and the theoretical residual based on vehicle status data, zero-relaxation ideal charging reference and dynamic available capacity envelope. The coupling control module is used to make topological coupling decisions based on the actual residuals and theoretical residuals, generate the optimal charging control commands, and send them to the charging actuator.

Images