Shared mobile charging system and method

Abstract

The invention relates to the technical field of charging facilities, and discloses a shared mobile charging system and method, and the system comprises a control execution module, an environment modeling module, a demand prediction module, a thermal field screening module, an optimization decision module, and a scheduling interlocking module. The environment modeling module establishes digital mapping of parking space, power grid phase and access cost; the demand prediction module calculates the predicted occupation duration of the vehicle; the thermal field screening module is used for eliminating thermal risk parking spaces based on neighborhood accumulated thermal potential energy; the optimization decision module determines an optimal target parking space in combination with space-time matching and power grid balance cost; the dispatching interlocking module generates a speed adjusting instruction according to the predicted arrival time difference of the vehicle and the mobile power supply unit so as to achieve time-space synchronization, and executes power derating according to a pre-starting signal of a stereo garage motor so as to achieve transient protection. According to the invention, active adjustment of the three-phase load of the power grid, avoidance of local heat accumulation and cooperative control of an electromechanical system are realized through multi-dimensional optimization and a dual interlocking mechanism.

Description

Technical Field

[0001] This invention relates to the field of charging infrastructure technology, specifically to a shared mobile charging system and method. Background Technology

[0002] With the continuous growth of electric vehicle ownership, parking and charging resources in urban centers are becoming increasingly strained. Automated parking garages, due to their high space utilization, are gradually becoming an important facility for solving parking difficulties. To meet users' needs for immediate charging while parking, integrating charging functions into automated parking garages has become an industry trend.

[0003] In the construction and operation of existing multi-level parking garage charging facilities, there is a significant contradiction between the density of physical space and the limited capacity of the power grid. Traditional construction methods typically involve installing fixed charging piles in each parking space. This approach not only results in high construction costs but also leads to low actual utilization rates of the charging facilities due to situations such as parking spaces being occupied by gasoline vehicles or electric vehicles not requiring charging.

[0004] Large-scale centralized charging poses a challenge to the stable operation of microgrids. In the high-density scenarios of automated parking garages, without refined management of grid phases, random vehicle access can easily lead to severe imbalances in the three-phase load of the grid, resulting in excessive neutral current or localized transformer overload. Furthermore, automated parking garages are typically semi-enclosed steel frame structures with limited ventilation and heat dissipation. Existing control strategies often focus on over-temperature protection for individual devices, lacking prediction and analysis of the overall thermal distribution within the garage. Simultaneous high-power charging of multiple vehicles in adjacent areas can easily cause localized heat accumulation, posing physical safety hazards.

[0005] The mechanical operation system and electrical charging system of automated parking garages typically operate independently, lacking a coordinated control mechanism. The vehicle platform lifting motor in an automated parking garage generates a large inrush current upon startup. If multiple charging loads operating at full power are present in the microgrid at this time, the superimposed power surge may cause a voltage drop on the bus or even trigger a circuit breaker, affecting the system's continuous operation. Existing scheduling strategies struggle to simultaneously ensure mechanical access efficiency while also considering the three-phase balance of the power grid, thermal safety distribution, and suppression of transient power surges. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a shared mobile charging system and method, which solves the problems of unbalanced three-phase load of the power grid, risk of local heat accumulation in semi-enclosed spaces, and lack of coordinated control between mechanical movements and charging load in existing multi-level parking garage charging facilities under high-density application scenarios.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a shared mobile charging system, comprising:

[0008] The control and execution module integrates the three-dimensional parking garage unit, the passive docking unit, the mobile power supply unit, and the shuttle transport unit to perform vehicle circulation and mobile charging services, and to provide real-time feedback on mechanical actions and electrical load status.

[0009] The environmental modeling module is used to construct a digital mapping of the physical system, store the static attributes of parking spaces, and update the grid load vector representing the load distribution of the three-phase power grid and the thermal field state vector representing the heat accumulation level of the parking space area in real time.

[0010] The demand forecasting module is used to calculate and quantify the expected occupancy time of the vehicle by combining the battery parameters of the vehicle to be charged with the rated power of the mobile power supply unit.

[0011] The thermal field filtering module is used to calculate the cumulative thermal potential energy in the neighborhood of the parking space based on the thermal field state vector, and to remove parking spaces whose cumulative thermal potential energy exceeds the safety threshold in order to generate a set of safe candidate parking spaces.

[0012] The optimization decision module is used to determine the optimal target parking space from the candidate parking space set by minimizing the comprehensive cost function that includes spatiotemporal matching and power grid balance terms, based on the expected occupancy time and the power grid load vector.

[0013] The scheduling interlock module is used to generate speed adjustment commands based on the expected arrival time difference between the vehicle and the mobile power supply unit to achieve time and space synchronization, and to generate power derating commands based on the pre-start signal of the lifting motor of the three-dimensional parking garage to achieve transient power interlock protection.

[0014] The control and execution module, serving as the physical foundation of the system, integrates the automated parking garage unit, passive docking unit, mobile power supply unit, and shuttle transport unit. The automated parking garage unit is responsible for high-density vehicle storage and circulation, and provides real-time feedback on the power status and pre-start signals of its internal lifting motors. The passive docking units are distributed throughout the charging operation level, serving as passive physical interfaces to connect with the mobile power supply units. The mobile power supply units perform energy conversion and output. The shuttle transport unit carries the mobile power supply units between parking spaces.

[0015] The environmental modeling module is used to construct a digital mapping of the physical system and store the static attributes of each parking space in the charging operation layer. These static attributes include spatial geometric coordinates, corresponding grid phase attributes, and mechanical access depth costs. Simultaneously, the environmental modeling module receives sensor data and updates in real time the grid load vector representing the three-phase grid load distribution, as well as the thermal field state vector representing the thermal accumulation level in each parking space area.

[0016] The demand forecasting module processes charging requests from vehicles, obtains vehicle battery parameters, and, in conjunction with the rated output power of the mobile power supply unit, calculates the estimated time required for the vehicle to complete the charging task. This estimated time transforms the vehicle's energy demand into a linear time-dimensional parameter, providing a quantitative basis for subsequent time-based scheduling.

[0017] The thermal field screening module performs initial screening of parking spaces based on physical safety constraints, using the thermal field state vector provided by the environmental modeling module. The thermal field screening module calculates the cumulative thermal potential energy in the neighborhood of each parking space in the charging operation layer. The calculation of the cumulative thermal potential energy is based on the principle of thermal superposition, taking into account the current temperature value, thermal coupling coefficient and distance attenuation factor of adjacent parking spaces in the neighborhood. The calculation results are compared with the preset safety threshold, and parking spaces with excessive cumulative thermal potential energy are eliminated to generate a set of candidate parking spaces that meet the thermal safety standards.

[0018] The optimization decision-making module determines the optimal target parking space from the candidate parking space set. Internally, it runs a comprehensive cost function, which includes a spatiotemporal matching cost term and a grid balancing cost term. The spatiotemporal matching cost term evaluates the matching degree between the vehicle's expected occupancy time and the mechanical access depth cost of the parking space, aiming to allocate vehicles with long-term charging to deeper parking spaces and vehicles with short-term charging to shallower parking spaces. The grid balancing cost term predicts the three-phase load change after the vehicle's access based on the grid phase attribute of the parking space and the current grid load vector. By minimizing the three-phase load variance, it determines the optimal phase access point and selects the parking space with the lowest comprehensive cost as the target parking space.

[0019] The scheduling interlock module combines mechanical scheduling and electrical protection functions. It calculates the estimated time for the automated parking system to move vehicles to the optimal target parking space, and the estimated time for the shuttle transport unit to transport the mobile power supply unit to the same location. Based on the time difference between these two times, it generates speed adjustment commands to control the operating speed of the unit expected to arrive earlier, achieving spatiotemporal synchronization between the vehicles and the mobile power supply unit, and monitoring the operating status of the automated parking system's lifting motor. When a motor pre-start signal is detected, it calculates the estimated surge power of the motor start-up and the grid's remaining margin, generates a power derating command, and sends it to the operating mobile power supply unit. The mobile power supply unit responds to the command by reducing its output power to offset the transient power surge generated by the motor start-up until the motor enters steady-state operation.

[0020] Furthermore, the passive docking unit is physically labeled as phase A, phase B, or phase C interface according to the external power grid line it is connected to. Based on the fixed phase mapping relationship of spatial location, the control system can actively adjust the three-phase load of the power grid by selecting the spatial location.

[0021] Furthermore, the mechanical access depth cost is used to quantify the standard time cost of a vehicle moving from a specific vehicle location to the exit. This parameter is calculated based on the horizontal and vertical rated speeds of the vehicle handling mechanism and the time consumed by stationary actions, transforming non-uniform spatial distances into uniform time metrics.

[0022] A shared mobile charging method includes the following steps:

[0023] S10 establishes a digital mapping of the charging operation layer, defines the spatial coordinates of each parking space, the grid phase and the mechanical access depth cost, and updates the grid load vector and thermal field state vector in real time.

[0024] S20: Read the battery parameters of the vehicle to be charged, and calculate the estimated occupancy time of the vehicle based on the battery parameters and the rated output power of the mobile power supply unit;

[0025] S30, calculate the cumulative thermal potential energy of each parking space neighborhood in the charging operation layer, remove parking spaces that do not meet the thermal safety threshold, and generate a candidate parking space set;

[0026] S40, construct a comprehensive cost function that includes a spatiotemporal matching cost term and a power grid balance cost term, calculate the cost value of each parking space in the candidate parking space set, and select the parking space with the smallest comprehensive cost value as the optimal target parking space;

[0027] S50, calculate the estimated time difference between the vehicle and the mobile power supply unit arriving at the optimal target parking space, and generate a speed adjustment command to drive the control execution module so that the vehicle and the mobile power supply unit arrive synchronously.

[0028] The S60 monitors the status of the lifting motor of the automated parking system during charging and generates a power derating command to limit the output power of the mobile power supply unit when a pre-start signal of the motor is detected.

[0029] This invention provides a shared mobile charging system and method. It has the following beneficial effects:

[0030] 1. This invention establishes a fixed mapping relationship between parking space coordinates and power grid phase attributes, and introduces a power grid balance cost term in the optimization decision-making process, thereby enabling proactive adjustment of the three-phase load distribution of the power grid through a parking space allocation strategy. This control method can guide vehicles to be charged to parking spaces corresponding to lightly loaded phases based on real-time load conditions, effectively reducing the three-phase imbalance in high-density centralized charging scenarios and improving the stability of microgrid operation.

[0031] 2. This invention utilizes a thermal field screening module to pre-screen parking spaces based on physical safety constraints. By calculating the cumulative thermal potential energy within the neighborhood and combining it with safety threshold judgments, it can identify and avoid areas of heat accumulation. This avoids high-power charging operations in parking spaces with poor heat dissipation or dense surrounding heat sources, prevents localized overheating inside the automated parking garage, and ensures physical safety during the charging process.

[0032] 3. This invention achieves spatiotemporal synchronization between devices by calculating the expected arrival time difference between the vehicle and the mobile power supply unit and adjusting the operating speed, thereby reducing ineffective waiting time. On the other hand, by using feedforward control to reduce the charging power at the moment of motor start-up, it offsets the transient power surge and ensures the stable operation of the system under limited power capacity. Attached Figure Description

[0033] Figure 1 This is a schematic diagram of the system framework of the present invention;

[0034] Figure 2 This is a schematic diagram of the method flow of the present invention;

[0035] Figure 3 This is a schematic diagram of the logic flow of the thermal field screening module of the present invention;

[0036] Figure 4 This is the control logic timing diagram of the scheduling interlock module of the present invention;

[0037] Figure 5 This is a comparison diagram of power grid imbalance according to the present invention;

[0038] Figure 6 This is a schematic diagram of the three-dimensional garage unit of the present invention;

[0039] Figure 7 This is a schematic diagram of the mobile power supply unit of the present invention.

[0040] Among them, 100 is the control execution module; 110 is the three-dimensional parking garage unit; 120 is the passive docking unit; 130 is the mobile power supply unit; 140 is the shuttle transportation unit; 200 is the environmental modeling module; 300 is the demand forecasting module; 400 is the thermal field screening module; 500 is the optimization decision module; and 600 is the scheduling interlock module. Detailed Implementation

[0041] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Please see the appendix Figure 1 This invention provides a shared mobile charging system, comprising:

[0043] The control execution module 100 forms the physical foundation of the system, integrating the automated parking garage unit 110, the passive docking unit 120, the mobile power supply unit 130, and the shuttle transport unit 140. The automated parking garage unit 110 physically includes a storage layer for long-term vehicle parking and a charging operation layer for vehicle turnover and charging services. The passive docking units 120 are distributed in each parking space of the charging operation layer, configured as passive interfaces containing only electrical contacts and detection contacts, and physically marked as A-phase, B-phase, or C-phase interfaces according to their connected external power grid lines. The mobile power supply unit 130 is configured as an independent movable device equipped with an AC / DC power conversion circuit. The shuttle transport unit 140 is positioned on the guide rails of the charging operation layer, used to carry the mobile power supply unit 130 for horizontal movement and to perform mechanical locking operations with the passive docking units 120. The automated parking garage unit 110 provides feedback on the position coordinates of the robotic arm, the power status of the lifting motor, and the pre-start signal. The passive docking unit 120 provides feedback on the three-phase voltage and current data of each node. The mobile power supply unit 130 provides feedback on its own output power and temperature data, as well as data from the battery management system of the vehicle to be charged, obtained through a handshake protocol. The shuttle transport unit 140 provides feedback on its real-time position and speed on the track.

[0044] The environmental modeling module 200 is connected to the control execution module 100 and is used to construct a digital mapping of the physical system. The environmental modeling module 200 stores the static attributes of each parking space in the charging operation layer. These static attributes include spatial geometric coordinates, corresponding power grid phase attributes, and mechanical access depth cost. The mechanical access depth cost represents the standard time cost for the automated parking garage unit 110 to move a vehicle from a specific parking space and transport it to the exit. The environmental modeling module 200 receives electrical and temperature data, calculates and updates in real time the power grid load vector representing the three-phase power grid load distribution, and the thermal field state vector representing the heat accumulation level in each parking space area.

[0045] The demand forecasting module 300 is used to process vehicle access requests, obtain the total battery capacity, current state of charge and target state of charge of the vehicle to be charged, and calculate the estimated time required for the vehicle to complete the charging task in combination with the rated output power of the mobile power supply unit 130. The estimated time is used as a parameter to quantify the vehicle's time dimension and is transmitted to the subsequent decision module.

[0046] The thermal field screening module 400 is connected to the environmental modeling module 200 and is used to perform initial screening of parking spaces based on physical safety constraints. According to the thermal field state vector, it calculates the cumulative thermal potential energy in the neighborhood of each parking space in the charging operation layer and compares it with the preset safety threshold. Parking spaces with excessive cumulative thermal potential energy are eliminated, and a set of available candidate parking spaces is generated by combining the vacancy status of the parking spaces.

[0047] The optimization decision module 500 is connected to both the demand forecasting module 300 and the thermal field screening module 400. It determines the optimal target parking space from the candidate parking space set. Internally, it runs a comprehensive cost function that includes a spatiotemporal matching cost term and a power grid balancing cost term. The spatiotemporal matching cost term evaluates the degree of matching between the expected vehicle occupancy time and the mechanical access depth cost of the parking space, while the power grid balancing cost term evaluates the dispersion of the three-phase load of the power grid after load connection. The optimization decision module 500 calculates and outputs the parking space number with the minimum comprehensive cost as the optimal target parking space.

[0048] The scheduling interlock module 600 connects the optimization decision module 500 and the control execution module 100, and has both mechanical scheduling and electrical protection functions. In terms of mechanical scheduling, it calculates the estimated vehicle transport time for the automated parking unit 110 to transport vehicles to the optimal target parking space, and the estimated module movement time for the shuttle transport unit 140 to transport the mobile power supply unit 130 to the same location. The scheduling interlock module 600 generates a speed adjustment command based on the difference between the two times, controlling the operating speed of the automated parking unit 110 or the shuttle transport unit 140, so that both reach the designated position within a preset time window. The scheduling interlock module 600 continuously monitors the operating status of the lifting motor in the automated parking unit 110. When it receives a motor pre-start signal, it calculates the remaining power margin of the current power grid and generates a power derating command. The power derating command is sent to the mobile power supply unit 130, which is in operation, to forcibly reduce its output power to offset the transient power impact generated by the motor start-up, until the motor enters steady-state operation.

[0049] See attached document Figure 2 , Figure 2 This is a flowchart of a shared mobile charging method according to an embodiment of the present invention. The present invention provides a shared mobile charging method, comprising the following steps:

[0050] S10, the environmental modeling module 200 establishes a digital model of the charging operation layer, defines the spatial coordinates of each parking space, the grid phase and the mechanical access depth cost, and updates the grid load vector and thermal field state vector in real time.

[0051] S20, the demand forecasting module 300 reads the battery parameters of the vehicle to be charged, and calculates the estimated occupancy time of the vehicle based on the battery parameters and the rated power of the mobile power supply unit 130.

[0052] S30, the thermal field screening module 400 calculates the cumulative thermal potential energy of the neighborhood of each parking space in the working layer, removes parking spaces that do not meet the thermal safety threshold, and generates a set of candidate parking spaces;

[0053] S40, the optimization decision module 500 constructs a comprehensive cost function, calculates the cost value of each parking space in the candidate parking space set, and selects the parking space with the smallest comprehensive cost value as the optimal target parking space;

[0054] S50, the scheduling interlock module 600 calculates the estimated time difference between the vehicle and the mobile power supply unit 130 arriving at the optimal target parking space, and generates a speed adjustment command to drive the control execution module 100 so that the vehicle and the mobile power supply unit 130 arrive synchronously.

[0055] S60, the scheduling interlock module 600 monitors the status of the lifting motor of the three-dimensional parking garage during the charging process, and generates a derating command to limit the output power of the mobile power supply unit 130 when a motor pre-start signal is detected.

[0056] The specific implementation principles of each module of the present invention will be explained in detail below with reference to the accompanying drawings.

[0057] In this embodiment, the control execution module 100 serves as the physical execution foundation of the system and adopts an architecture design of storage-computation separation and mobile energy supply. It mainly consists of a three-dimensional parking garage unit 110, passive docking units 120 arranged in an array, a schedulable mobile power supply unit 130, and a shuttle transport unit 140.

[0058] The automated parking garage unit 110 is physically divided into a high-density storage layer and a charging operation layer. In this embodiment, the high-density storage layer is located in the upper middle or rear area of ​​the automated parking garage unit 110. This area is configured as a pure vehicle parking space, without high-power charging cables, and only retains vehicle handling machinery interfaces. The charging operation layer is located at the bottom of the automated parking garage unit 110 or on a specific operating plane that facilitates vehicle entry and exit, and is planned as a matrix-like grid space, with each grid node corresponding to a standard parking space. The automated parking garage unit 110 is equipped with a vehicle handling mechanism, which can specifically adopt a stacker crane or a vertical lift structure, to respond to coordinated scheduling commands and physically move vehicles between the high-density storage layer and the charging operation layer.

[0059] The passive docking unit 120 serves as the direct physical interface for vehicle charging and is located beside each parking space in the charging operation layer. In this embodiment, the passive docking unit 120 is configured as a passive terminal, which is not connected to an external power source and does not contain active power electronic units such as AC / DC rectifier modules or DC / DC converter modules. One end of the passive docking unit 120 is equipped with a standard charging gun or automatic charging connection mechanism extending into the parking space for establishing a physical connection with the electric vehicle parked in that space; the other end is equipped with a high-current receiving interface and signal interaction contacts for waiting for the mobile power supply unit 130 to connect. When not docked with the mobile power supply unit 130, the passive docking unit 120 is in a standby state with no potential.

[0060] The mobile power supply unit 130 is configured as an independent, modular active power conversion device, integrating a high-power AC / DC conversion circuit, a charging controller, and thermal management components. Its output terminal is equipped with a floating power connector male that matches the passive docking unit 120. After the mobile power supply unit 130 physically docks with the passive docking unit 120, the mobile power supply unit 130 injects the converted DC power into the passive docking unit 120 through a power receiving interface, thereby charging the electric vehicle through the passive docking unit 120.

[0061] In the charging operation layer, the power supply interfaces at each parking space are connected to the A-phase, B-phase, and C-phase lines of the power grid according to a preset spatial distribution pattern (e.g., alternating columns or grouped by area). The system assigns a unique physical address ID to each parking space, which is associated with a specific phase attribute in the environment modeling module 200 through a lookup table or database binding. If the parking space coordinates of charging operation layer 12 are defined as... When the mobile power supply unit 130 moves to this coordinate and connects to the power supply network, the phase of the power grid connected to its input terminal will be... It is fixed and known, that is, it satisfies This fixed phase mapping relationship based on spatial location allows the upper-level control system to control the spatial location of the vehicle and the mobile power supply unit 130. This allows for the indirect selection of the phase connected to the power grid, thereby enabling proactive adjustment of the three-phase load balance.

[0062] The shuttle transport unit 140 is configured on a dedicated running track in the charging operation layer, which is laid parallel to the parking space arrangement direction. The shuttle transport unit 140 specifically includes a horizontal drive mechanism and a telescopic docking mechanism. The horizontal drive mechanism is used to support the planar displacement of the mobile power supply unit 130 in the X-axis or Y-axis direction between different parking spaces. The telescopic docking mechanism is used to extend the mobile power supply unit 130 along the Z-axis direction (i.e., perpendicular to the track direction) after the shuttle transport unit 140 reaches the designated parking space coordinates, so that it physically couples with the passive docking unit 120 at that parking space.

[0063] In this embodiment, the environmental modeling module 200 serves as the core data processing hub of the system, configured to construct a digital mapping model for the control execution module 100. This module transforms discrete sensor data at the physical level into standardized vectors that can be computed at the logical level by establishing a high-precision multi-dimensional state space. Its internal logic specifically includes a static attribute library construction unit, a power grid dynamic sensing unit, and a thermal field state mapping unit.

[0064] The static attribute library construction unit is configured to store and manage the inherent physical attributes of each parking space in the charging operation layer. In this embodiment, for any parking space within the charging operation layer... The static attribute library building unit establishes a feature descriptor containing triple information. .in, This represents the three-dimensional geometric coordinates of the parking space in the coordinate system of the automated parking garage. ); This indicates the phase attribute of the power grid connected to the passive docking unit 120 corresponding to the parking space, and its value set is as follows: The phase attributes of the power grid are pre-entered and fixed by the hardware construction wiring diagram; Defined as mechanical access depth cost, it is used to quantify the physical time cost of the automated parking garage unit 110 moving a vehicle from the vehicle and transporting it to the garage exit.

[0065] Mechanical access depth cost This is a key benchmark variable for achieving spatiotemporal coordinated control in this invention. Its physical significance lies in mapping non-uniform spatial distance to a uniform time metric, thereby enabling subsequent optimization algorithms to make decisions based on time efficiency rather than simply geometric distance. In this embodiment, the vehicle transport mechanism adopts a motion mode combining horizontal movement and vertical lifting actions. The static attribute library construction unit is based on the rated horizontal speed of the vehicle transport mechanism. Rated vertical speed And the time consumed by the fixed object retrieval action The following kinematic formula is used to calculate the kinematics of each parking space. Mechanical access depth cost :

[0066] ;

[0067] In the formula, This indicates the reference coordinates of the entrance and exit of the automated parking garage. The function represents the horizontal and vertical movements of the vehicle handling mechanism as parallel actions, and the total time depends on the movement time of the longer dimension. This represents the inherent time constant required for the extension, gripping, and locking actions of the robotic arm. This constant is determined by the physical travel of the mechanical mechanism and the response characteristics of the drive motor. Indicates the parking space number. Indicates parking space Horizontal coordinates Indicates parking space The vertical coordinates, Indicates the horizontal rated speed. This indicates the vertical rated speed.

[0068] In this embodiment, a preset fixed positive real number is used. For handling systems employing multi-stage relays or other complex mechanical structures, those skilled in the art can adjust the above calculation logic based on the corresponding kinematic equations. Through the above calculations, the system generates a time cost grid map covering the entire garage, where parking spaces located deeper in the physical location have higher time costs. The value is lower, while parking spaces near the entrance and exit have lower values. value.

[0069] The power grid dynamic sensing unit is configured to monitor and vectorize the operating status of the power grid in real time, and to construct a power grid load vector. This vector not only reflects the internal load of the charging system but also reflects the background load of the power grid by collecting electrical parameters at the main incoming line, thus providing a global perspective for subsequent peak shaving and valley filling and three-phase balancing. This unit collects power meter data at the power grid incoming cabinet or main bus via a high-speed bus, with the data sampling frequency configured to satisfy the Nyquist sampling theorem to capture transient changes. In this embodiment, the power grid dynamic sensing unit defines the time... grid load vector A column vector containing the characteristics of three-phase complex power:

[0070] ;

[0071] In the formula, These represent the apparent complex power of phases A, B, and C, respectively. These represent the phasor data of the three-phase voltages; These represent the conjugate phasor data of the three-phase currents. By using complex power calculation, the grid load vector can simultaneously include active and reactive power information, ensuring that the system can comprehensively consider the influence of the power factor when adjusting three-phase imbalance.

[0072] The thermal field state mapping unit is configured to construct thermal field state vectors characterizing the thermodynamic distribution of the garage. Considering the high-density, enclosed nature of automated parking garages, localized heat accumulation is a significant factor affecting charging safety. The thermal field state mapping unit aggregates temperature sensor data from the passive docking unit 120, the mobile power supply unit 130, and environmental monitoring points within the parking garage to discretize the temperature of the parking space. It is assumed that the charging operation layer has... For each parking space, the thermal field state mapping unit will map the thermal field state vector. Defined as a 3D column vector:

[0073] ;

[0074] In the formula, Indicates the first Each parking space area at any time The equivalent temperature value. The logic for determining the value is as follows: when the parking space When in an idle state, The value is taken as the real-time reading of the ambient temperature sensor in that area; when the parking space While charging is in progress, The value is the temperature of the internal power devices in the mobile power supply unit 130. Temperature of external exhaust vent The weighted sum, i.e. ,in The preset weighting coefficients, ranging from 0 to 1, are used to balance the weights of the equipment's internal temperature and the influence of the environment. The thermal field state mapping unit 230 filters the collected raw temperature data to remove measurement noise and ensure that the thermal field state vector accurately reflects the current heat load distribution in the physical space, thereby supporting the subsequent calculation of the safe neighborhood potential energy by the thermal field screening module 400.

[0075] In this embodiment, the demand forecasting module 300 serves as an interface unit connecting the vehicle's physical state and the system scheduling logic. It is configured to convert the vehicle's nonlinear energy replenishment demand into linear time occupancy parameters that can be scheduled by the system. By parsing the vehicle's battery management system (BMS) data through a standardized communication protocol and combining it with the power output characteristics of the mobile power supply unit 130, a mapping model from energy shortage to expected occupancy time is established. Its internal processing logic is specifically executed by the parameter parsing subunit and the duration estimation subunit.

[0076] The parameter parsing subunit is configured to acquire vehicle status data during the access interaction phase when a vehicle enters the automated parking garage. In this embodiment, the parameter parsing subunit extracts and locks a set of key status parameters through an interactive terminal located at the garage entrance, a cloud data interface associated with license plate recognition, or preset information from the user's mobile device. This parameter set specifically includes: the current state of charge. This indicates the remaining battery charge percentage; target state of charge. This indicates the user-defined or system default percentage of battery capacity at which charging is stopped; battery health status. This represents the ratio of the current actual battery capacity to the rated capacity, used to correct capacity estimates for aging batteries; the battery's rated total energy. (Or calculated by multiplying the rated voltage by the rated ampere-hour capacity); and the maximum permissible charging voltage. and maximum allowable charging current These two parameters define the safe charge-receiving boundaries of the battery under the current temperature and conditions.

[0077] The duration estimation subunit is based on the above parameter set and combined with the rated output power of the mobile power supply unit 130 itself. Calculate the estimated time required for the vehicle to complete the charging task. This calculation process not only considers the energy supply and demand difference, but also integrates power constraints and the nonlinear characteristics of the charging curve. The physical principle is that the charging time is not simply determined by the battery's low charge level, but is also limited by the small power bottleneck between the mobile power supply unit 130 and the vehicle BMS, and time compensation is needed for the natural power decay during the constant voltage charging phase.

[0078] Based on this principle, the duration estimation subunit first determines the effective charging power of this charging operation. This value is the smaller of the output capability of the mobile power supply unit 130 and the power receiving capability of the vehicle battery. .

[0079] Subsequently, the duration estimation subunit calculates the estimated duration using the following formula, based on the principle of energy conservation and the charging curve correction coefficient. :

[0080] ;

[0081] In the formula, The rated total energy of the battery, expressed in kilowatt-hours. This is the battery health status coefficient, with a value ranging from 0 to 1. This indicates the percentage difference in battery power to be replenished. This represents the overall charging efficiency of the system, encompassing AC / DC conversion losses and line losses. In this embodiment, it is set as a fixed constant between 0.90 and 0.98. This is a charging curve correction factor used to compensate for the time delay caused by the power drop during the transition from the constant current charging (CC) stage to the constant voltage charging (CV) stage. The charging curve correction factor is related to the target state of charge. The increasing function typically takes values ​​between 1.1 and 1.5. The fixed dead time covers the inherent time overhead required for system handshake, insulation detection, pre-charging, and settlement and unlocking processes after charging is completed. It is usually a preset minute-level constant.

[0082] Through the above calculation logic, the demand forecasting module 300 quantifies the charging demand, which originally only had electrical attributes, into a demand with clear time boundaries. The time dimension weights are directly input into the subsequent multi-objective global optimization decision module 500, enabling the system to assess the time resource costs of different vehicles before they actually enter the warehouse. The specific values ​​are determined by a pre-stored library of standard charging characteristic curves based on different battery chemistry systems within the system. Those skilled in the art can obtain a lookup table for the charging curve correction coefficients by fitting data from the battery manufacturer's technical manual or historical charging big data; details will not be elaborated here. This estimation model, based on actual operating conditions, ensures that the time parameters relied upon by the scheduling system closely approximate the real physical operation process, avoiding resource conflicts caused by estimation errors.

[0083] See attached document Figure 3 , Figure 3 A schematic diagram of the logic flow of a thermal field screening module according to an embodiment of the present invention is shown. In this embodiment, the thermal field screening module 400 operates before multi-objective optimization decision-making. By calculating the thermal potential energy of parking spaces and their neighborhoods, it screens out a set of available parking spaces that meet thermal safety standards, thereby reflecting the thermodynamic dimension in multi-dimensional spatiotemporal collaborative control. Its internal processing logic is specifically executed by a neighborhood thermal potential energy calculation subunit and a safety threshold determination subunit.

[0084] The neighborhood thermal potential energy calculation subunit is configured to quantify the thermal risk of each vacant parking space in the charging operation layer. Its physical principle is based on the superposition principle and diffusion law in thermodynamics: in a semi-enclosed space like a multi-level parking garage, the ambient temperature of a parking space depends not only on its own heat dissipation conditions but also on the radiation and convection from surrounding heat sources, and this influence decreases non-linearly with increasing distance. Therefore, this embodiment introduces the physical quantity of neighborhood thermal potential energy to characterize the degree to which the target parking space is affected by the thermal radiation and convection from surrounding already-operated parking spaces when charging operations are carried out in the future.

[0085] For any parking space to be evaluated in the charging operation layer The neighborhood thermal potential energy calculation sub-unit first defines its thermal influence neighborhood set. .gather Defined as parking space All adjacent parking spaces within a specific spatial range centered on the center; in this embodiment, the parking spaces are typically selected from those adjacent to the center. Euclidean distance The coordinates of all parking spaces within a preset value (e.g., 3 to 5 meters). Then, based on the thermal field state vector provided by the environment modeling module 200. The following discrete superposition formula is used to calculate parking spaces. Cumulative neighborhood thermal potential ;

[0086] ;

[0087] In the formula, Indicates the target parking space The current reference temperature value (derived from the corresponding component of the thermal field state vector). Indicates the number of neighbors within the neighborhood. The current temperature value of each adjacent parking space. Indicates adjacent parking spaces With the target parking space The physical Euclidean distance between them The characteristic length constant for thermal diffusion is used to control the rate at which the thermal effect decays with distance. Its value depends on the airflow rate and partition structure inside the garage. In this embodiment, The value range is typically set to 2.0 to 5.0 meters. Defined as the thermal coupling coefficient, this is a dimensionless weighted value used to describe the differences in heat conduction at different spatial orientations (e.g., up, down, left, right). Considering the upward flow of hot air due to natural convection, the target parking space... The adjacent parking space directly below the target parking space has the greatest thermal impact, therefore its The value is typically between 1.2 and 1.5; while parking spaces located on the horizontal side... The value is typically between 0.8 and 1.0; parking spaces located vertically above are less affected by hot air currents. The value is typically between 0.3 and 0.5.

[0088] The safety threshold determination subunit is configured to perform physical-thermal dual constraint screening based on the calculated neighborhood thermal potential energy, and has a preset thermal safety threshold. This thermal safety threshold The determination logic follows the "barrel effect" principle, that is, taking the minimum value among the temperature resistance limit of the insulation material of the passive docking unit 120, the over-temperature protection threshold of the power device of the mobile power supply unit 130, and the thermal runaway critical temperature of the power battery pack, and subtracting a reserved safety margin (e.g., 5°C to 10°C) from this value. In this embodiment, The typical value range is 45℃ to 60℃.

[0089] During the screening process, the safety threshold determination subunit traverses all physically unoccupied vacant parking spaces. For any vacant parking space... If it meets the conditions This indicates that charging operations at this location will not cause localized overheating and have sufficient thermal buffer margin. Parking spaces that meet the above conditions are marked as thermally safe parking spaces, and their indices are added to the set of safe candidate parking spaces. In a formal sense, the set of safe candidate parking spaces. The construction logic can be represented as:

[0090] ;

[0091] In the formula, For parking spaces The physical status flag bit, when This indicates that the parking space is physically vacant and without malfunction. This represents a logical AND operation. Through this mechanism, the hotspot screening module 400 effectively eliminates high-risk parking spaces that, while physically vacant, are located in hotspot areas. This process reduces the originally massive search space across the entire parking garage to a smaller one. Set, and put the The set is passed as input to the subsequent multi-objective global optimization decision module 500. This not only significantly reduces the computational complexity of the optimization algorithm, but also prevents the risk of thermal chain reaction caused by improper parking space allocation at the source at the physical level, thus achieving proactive safety protection.

[0092] In this embodiment, the optimization decision module 500 serves as the core control center of the system. It is configured to perform optimal vehicle-parking space matching calculations based on the state space provided by the environment modeling module 200 and the safe candidate set output by the thermal field screening module 400. By constructing a multi-objective evaluation function that includes spatiotemporal matching degree and power grid balance degree, the dual optimization of logistics efficiency and power grid stability is achieved. Its internal logic is specifically executed by the spatiotemporal matching calculation subunit, the power grid balance optimization subunit, and the comprehensive decision-solving subunit.

[0093] The spatiotemporal matching calculation subunit is configured to calculate the spatiotemporal matching cost for each candidate parking space. Its core control strategy lies in implementing a storage and retrieval rule of long-term deep parking and short-term shallow parking to maximize the throughput efficiency of the automated parking system. Its physical principle utilizes the time masking effect of long charging times: allocating deep parking spaces with longer retrieval times to vehicles that have been parked for extended periods ensures that the long stroke time of the robotic arm is included within the vehicle's charging waiting time. This avoids high-frequency, long-distance handling caused by short-term vehicle occupancy of deep parking spaces, thus eliminating the efficiency bottleneck of the mechanical storage and retrieval system.

[0094] Therefore, in this embodiment, the spatiotemporal matching calculation subunit introduces a normalized deviation algorithm to adjust the estimated occupancy time output by the demand forecasting module 300. The mechanical access depth cost provided by the environment modeling module 200 Perform dimensional alignment. For the set of safe candidate parking spaces... Any parking space The spatiotemporal matching calculation subunit calculates the spatiotemporal matching cost using the following formula. :

[0095] ;

[0096] In the formula, This represents the estimated duration of time that the vehicles currently waiting to be charged will occupy. The maximum allowable charging time reference value set for the system is set to 10 to 12 hours in this embodiment to cover the typical overnight parking time. For parking spaces Mechanical access depth cost, The cost of the maximum mechanical access depth for the deepest parking space in a multi-level parking garage is determined by the physical dimensions of the garage. This indicates the absolute value operation. This is a sensitivity index used to adjust the penalty for non-matching items, and its value typically ranges from 1.5 to 3.0 (e.g., 2). When the relative time requirement of a vehicle is proportional to the relative depth cost of a parking space, A value close to 0 indicates optimal resource matching.

[0097] The grid balancing optimization subunit is configured to calculate the grid balancing cost for each candidate parking space. Its core objective is to proactively offset the three-phase imbalance components of the current power grid by utilizing the newly added charging load. This sub-unit uses the fixed mapping relationship between parking space and phase established in the environment modeling module 200 to predict the impact on the total power grid load after a vehicle is connected to a certain parking space.

[0098] Assume that the apparent power amplitudes of the three-phase loads A, B, and C of the power grid at the current moment are respectively , , For candidate parking spaces The system queries its corresponding phase. Assuming vehicles are assigned to parking spaces. The system obtains the effective charging power calculated by the vehicle's demand forecasting module 300. As a new load modulus (Here, the power factor is approximated as 1, or calculated based on vector operations). The system calculates the predicted three-phase load state after connection. For example, if the parking space... If it belongs to phase A, then the predicted load is... The power grid balancing optimization subunit constructs the following cost function based on the principle of minimizing the variance of the three-phase load:

[0099] ;

[0100] In the formula, , , Assuming the vehicle is connected to the parking space The subsequent three-phase predicted load value, The three-phase average load under the predicted state, i.e. Through this calculation logic, if the parking space If the phase currently has the lightest load, then the three-phase difference will decrease after a new load is added. The value is relatively low. This allows each vehicle to be charged to be treated as a schedulable balancing weight, dynamically smoothing out the three-phase fluctuations of the power grid at the physical level through parking space allocation logic.

[0101] The integrated decision-making sub-unit is configured based on the costs of the two dimensions mentioned above, and a global total cost function is constructed. The optimal solution is then obtained. Considering that spatiotemporal efficiency and grid quality have varying importance during different operating periods, dynamic weighting coefficients are introduced. and For any candidate parking space The global total cost function is defined as:

[0102] ;

[0103] In the formula, and These are the normalized baseline values ​​for the spatiotemporal matching cost and the power grid balance cost, respectively. They are usually taken as the maximum value of the corresponding cost in the current candidate set to ensure that the two indicators are normalized to the [0,1] interval and to prevent one indicator from dominating the optimization process due to differences in dimensions. , For the weighting coefficients, satisfying In this embodiment, and The determination is based on the grid load factor. Piecewise linear function: when (In light load condition) setting , The system prioritizes optimizing access efficiency; when (In overload mode) setting , The system prioritizes ensuring grid balance; within the 50% to 80% range, coefficients are transitioned via linear interpolation.

[0104] The comprehensive decision-making sub-unit traverses the set of safe candidate parking spaces. Calculate the corresponding parking spaces in the dataset. And select the parking space that minimizes the total cost. Assigning parking spaces as the ultimate goal:

[0105] ;

[0106] In the formula, Indicates the optimal parking space number Indicates the candidate parking space number. This represents the value of the independent variable that minimizes the objective function. This represents the minimum value operation. This represents the set of safe candidate parking spaces.

[0107] Once the optimal parking space is determined The optimization decision module 500 generates a list containing the target coordinates. The system issues control commands and sends them to the control execution module 100, triggering the coordinated action of the vehicle handling mechanism and the shuttle transport unit 140.

[0108] See attached document Figure 4 , Figure 4 A timing diagram of the control logic of a scheduling interlock module according to an embodiment of the present invention is shown. In this embodiment, the scheduling interlock module 600, as a key link connecting the decision-making layer and the execution layer, is configured to simultaneously solve the asynchronous coordination problem in physical space and the transient power surge problem in the electrical system. The scheduling interlock module ensures dual interlocking of the vehicle transport mechanism and the mobile power supply unit 130 in both spatiotemporal and energy dimensions through dual-thread time calculation and feedforward power control. Its internal logic is specifically divided into two levels: asynchronous spatiotemporal concurrent control and transient power interlock protection.

[0109] The dual-threaded time measurement subunit is configured to perform parallel calculations of the arrival of the vehicle and the mobile power supply unit 130 at the target parking space. The theoretically shortest time required. Receive the target parking space coordinates from the optimization decision module 500, and combine them with the real-time position of the current vehicle handling mechanism ( The real-time location of the mobile power supply unit 130 ( ) ), and plan the movement paths of the two respectively.

[0110] In this embodiment, the dual-thread time measurement subunit uses a trapezoidal velocity programming model for kinematic estimation. Considering that the actuators of a multi-level parking garage typically involve motion in two dimensions—horizontal (x-axis) and vertical (z-axis)—the total physical travel of the path in this embodiment is... The calculation uses Chebyshev distance (for simultaneous arrival of multiple axes) or Manhattan distance (for sequential actions on a single axis). Here, we take Manhattan distance as an example. .

[0111] For vehicle transport tasks, calculate their estimated arrival time. For the mobile power supply unit 130's delivery mission, calculate its estimated arrival time. The general calculation formula is as follows:

[0112] ;

[0113] In the formula, The total physical distance of the planned route. In this embodiment, the rated maximum cruising speed of the actuator is set to 1.5 to 2.0 m / s for the vehicle transport mechanism and 2.5 to 3.5 m / s for the mobile power supply unit 130 shuttle. The rated acceleration of the actuator is typically determined by the motor torque characteristics and is set to 0.5 to 1.0 m / s². To assist in determining the duration of actions, including the inherent time constants of mechanical movements such as alignment, lifting, and handover, which are typically fixed empirical values ​​(e.g., 10 to 15 seconds), the system can accurately obtain the time reference for both actions in full-speed operation mode.

[0114] The speed adjustment command generation subunit is configured to be based on time difference. Active speed intervention is executed. The speed adjustment command generation subunit first calculates the expected time deviation between the two. At the same time, a synchronization tolerance threshold is set. In this embodiment, the threshold value is between 2.0 and 5.0 seconds, and the threshold should be greater than the sum of the system communication delay and the servo control cycle. If This indicates that the two are naturally synchronized, and the system issues a full-speed operation command. If This indicates a significant timing mismatch, requiring deceleration planning for the party expected to arrive earlier to accommodate the party arriving later.

[0115] Assumption The original estimated time for the fast node. The original estimated time for the slow node (i.e. To achieve smooth, zero-wait transitions, the speed adjustment command generation subunit employs a full-stroke speed scaling strategy to reconstruct the running commands of fast nodes. It calculates the derating cruise speed command. The formula is as follows:

[0116] ;

[0117] In the formula, This represents the time that the fast node was originally used for pure motion. This represents the time spent on pure motion at the slow node. The motion time of the fast node is stretched to match that of the slow node, thereby calculating the required reduced cruising speed.

[0118] To ensure the low-speed stability of the mechanical system, this embodiment also presets a minimum stable operating speed. (For example, 0.1 to 0.3 m / s). If the calculated The system will then maintain Run it and add a delay before startup. :

[0119] ;

[0120] In the formula, This indicates the additional delay time added before startup. This represents the total target duration divided by the slow cycle time under the low-speed stable strategy. Indicates the auxiliary time constant. Indicates the distance / journey to be completed. This indicates the preset minimum stable operating speed. Indicates the system acceleration. Indicates the distance traveled at the minimum speed Time, Indicates acceleration from 0 to Time required.

[0121] This control improves the overall throughput of the automated parking system on the one hand, and on the other hand, it optimizes the motor power characteristics (e.g., or This significantly reduces instantaneous power demand and mechanical wear.

[0122] At the transient power interlock protection level, this embodiment employs a protection mechanism based on feedforward control theory to address the risk of grid slippage caused by the superposition of the inrush current during the start-up of a high-power boost motor and the charging load. The physical principle is as follows: due to the physical time delay in the establishment of the motor starting current (typically tens of milliseconds of magnetic field establishment time), this time difference is utilized to limit capacitive or resistive charging loads before the inductive load actually impacts the grid, thereby maintaining the dynamic balance of the total apparent power of the microgrid.

[0123] The motor pre-start monitoring subunit is configured to detect changes in the power state of the vehicle transport mechanism in real time. To overcome the physical limitation of the current sensor's response lag, this embodiment directly connects to the control bus of the frequency converter (VFD) or the instruction queue of the PLC. The motor pre-start monitoring subunit monitors specific trigger signals. The trigger signal Specific types include the inverter DC bus precharge completion signal, the brake release pre-lead signal, or the rising edge of a motion enable command from the upper-level controller. Once detected... When the flag is set, the system immediately determines that the power grid is about to enter a transient high-load area and sends an interrupt-level power freeze command to subsequent units.

[0124] The dynamic power peak shaping subunit is configured to calculate and execute a rapid derating of the global charging power upon receiving a trigger signal. The dynamic power peak shaping subunit first reads the current grid's rated capacity. The total real-time charging power of all 130 mobile power supply units. And the estimated transient surge power required for motor startup In this embodiment, It is not a fixed value, but a dynamic peak value obtained by referring to the pre-stored motor starting characteristic curve based on the motor's current load weight and lifting height. It is typically 1.5 to 2.5 times the rated power. To ensure the power grid does not trip due to overload, if the current... The dynamic power peak-shaving subunit calculates the global power derating factor using the following formula. :

[0125] ;

[0126] In the formula, For non-adjustable base load power such as garage lighting and control systems, As a safety margin factor, taking into account measurement errors and line losses, it is typically taken as 1.05 to 1.15 in this embodiment. The number of mobile power supply units currently in operation is 130. The function restricts the calculation result to a closed interval between 0 and 1. Indicates the summation index. No. The real-time charging power of each unit. If the calculation result is 0, it means that all charging tasks must be suspended to prioritize motor startup.

[0127] Calculate Subsequently, the dynamic power clipping subunit generates an adjusted current limiting command and sends it to the DC / DC converter of each mobile power supply unit 130. Each mobile power supply unit 130 limits the output current according to the coefficient. Adjusted to The total response time of the process (including communication delay and command execution time) is strictly designed to be less than the rise time constant of the motor stator current (usually less than 50ms), thereby suppressing the peak of the charging load at the physical level and reserving power margin for motor startup.

[0128] The power recovery hysteresis control subunit is configured to smoothly restore charging power after the motor enters steady-state operation or stops, preventing secondary grid oscillations caused by sudden load changes. The power recovery hysteresis control subunit introduces a hysteresis time. With recovery slope Two control parameters. When the motor pre-start monitoring subunit detects that the motor current has dropped below the rated value or an operation termination signal is received, the power recovery hysteresis control subunit first maintains the current derating state. In this embodiment, time The value ranges from 200ms to 500ms, and the derating state continues. The timing is set to cover the oscillation decay period during motor startup. Subsequently, the power limit is gradually released according to the following logic:

[0129] ;

[0130] In the formula, The power limit for the current control cycle. The power limit for the previous cycle; The original target power requested by the vehicle's BMS. To control the cycle, In this embodiment, the preset power recovery rate is used. The value is determined based on the short-circuit impedance ratio of the grid transformer and the allowable voltage fluctuation rate, and is usually set to restore 10% to 20% of the rated load per second, corresponding to a value range of 5kW / s to 20kW / s. Through this linear or S-shaped curve soft recovery strategy, the system avoids voltage flicker caused by multiple charging units simultaneously applying sudden loads, ensuring the power quality stability of the microgrid system.

[0131] To enable those skilled in the art to better understand the present invention, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0132] To verify the practical application effect and security of this system, this embodiment constructs a digital simulation three-dimensional parking garage scene that includes a storage layer and a bottom charging layer, and conducts a comparative experiment with the traditional "nearby allocation" scheduling strategy.

[0133] This embodiment simulates a multi-level parking garage with a capacity of 100 parking spaces.

[0134] Spatial Layout: The ground floor charging level has 50 charging spaces (10×5), with coordinates defined as follows: ,in , The upper part is the storage layer.

[0135] Electrical topology: The passive docking units 120 of the charging layer parking spaces are arranged alternately according to the ABC phase sequence. The specific mapping function is: If %3==0 connected to phase A, %3==1 connected to phase B, %3==2 connected to phase C.

[0136] Mobile Power Supply Unit 130 (MPU): Equipped with 10 autonomous mobile power supply robots, rated power 60kW, and equipped with flywheel energy storage modules to provide physical inertia.

[0137] Thermal environment: The initial ambient temperature of the garage is uniformly distributed at 25℃, and the distance between parking spaces is 2.5 meters.

[0138] The system starts up and loads the electrical phase map and thermal field sensor network of the charging layer. It monitors the three-phase load of the power grid in real time, under the initial state... , , .

[0139] Demand response and phase locking: An electric vehicle (target SOC 80%, required power 60kW) requests charging at the parking garage entrance. The demand forecasting module 300 intervenes: calculating the current three-phase imbalance. It finds that phase B has the highest load and phase C has the lowest load (110kW). The system locks phase C as the target phase and selects all available parking spaces connected to phase C as the initial selection set.

[0140] Dynamic thermal potential energy screening, with the thermal field screening module 400 intervening: Thermal potential energy calculations are performed on the parking spaces in the initial selection set. Assume there are two candidate parking spaces in the initial selection set:

[0141] Parking space P1 (coordinates 2, 2): Within its neighborhood (radius 3 meters), there are two high-power vehicles charging. Calculate the thermal potential energy. =38℃ (including predicted temperature rise).

[0142] Parking space P2 (coordinates 8, 5): Located in a cold zone, with no neighboring vehicles. Calculate its thermal potential. =28℃. Set a safety threshold. =45℃. Although P1 did not exceed the limit, P2 had a lower thermal potential.

[0143] Multi-objective optimization and execution, with the optimization decision module involving 500 interventions, calculates the cost function based on the shuttle's current position. Although P2 is slightly farther away, considering thermal safety redundancy and grid balance, the system ultimately decides to move the vehicle to parking space P2. The command is issued: the robotic arm of the automated parking system moves the vehicle from the storage layer to the bottom parking space P2; the shuttle moves the MPU to P2 and completes physical coupling, initiating charging.

[0144] Comparative example (traditional strategy): After a vehicle enters the parking lot, the robotic arm moves it to the nearest available parking space on the ground floor for charging, without considering the phase distribution of the power grid or performing thermal field prediction (relying solely on the temperature-controlled fan of a single parking space for passive heat dissipation).

[0145] Example (Strategy of the Invention): The above-described phase balance and thermal potential energy screening algorithm is used for scheduling.

[0146] The experimental data are shown in Table 1:

[0147] Table 1. Statistics on Charging Efficiency and Safety

[0148] index Comparative Example Example illustrate Maximum current difference between three phases 180A 25A This solution effectively reduces neutral current and line loss. Number of local overheating alarms 5 times 0 times The traditional solution requires five triggers of emergency power reduction to cool down the temperature. Average charging power 42kW 56kW Due to its excellent thermal management, this solution does not require frequent derating and has a higher average power output.

[0149] Analysis of power grid load balancing capability:

[0150] See appendix Figure 5 Comparative analysis (dashed line): When using the traditional physical distance priority strategy, the curve exhibits a high-amplitude oscillation. Due to the randomness of vehicle entry, it is very easy for multiple vehicles to be continuously assigned to the same phase (such as phase A), resulting in a drastic fluctuation in the imbalance between 20% and 35%, with the average value remaining at a high level. This puts a great deal of thermal load pressure on the neutral line of the garage transformer.

[0151] Implementation Results (solid line): After adopting the phase topology mapping and active optimization strategy described in this invention, the curve is significantly flattened. Regardless of the vehicle's access frequency, the system can always dynamically lock onto the phase with the lowest current load for matching. As shown in the figure, the imbalance is always controlled within 5%, achieving peak shaving and valley filling effects on the grid side.

[0152] The control system based on dynamic thermal field and phase topology provided by this invention reduces grid imbalance by approximately 85% and lowers local hotspot peak temperatures by 23°C compared to traditional scheduling schemes in high-density charging scenarios in automated parking garages. Through an active spatial discretization strategy, it not only ensures physical safety but also indirectly improves the overall charging throughput efficiency of the parking garage by avoiding overheating derating.

Claims

1. A shared mobile charging system, characterized in that, include: The control and execution module integrates the three-dimensional parking garage unit, the passive docking unit, the mobile power supply unit, and the shuttle transport unit to perform vehicle circulation and mobile charging services, and to provide real-time feedback on mechanical actions and electrical load status. The environmental modeling module is used to construct a digital mapping of the physical system, store the static attributes of parking spaces, and update the grid load vector representing the three-phase grid load distribution and the thermal field state vector representing the thermal accumulation level of the parking space area in real time. The demand forecasting module is used to calculate and quantify the expected occupancy time of the vehicle by combining the battery parameters of the vehicle to be charged with the rated power of the mobile power supply unit. The thermal field filtering module is used to calculate the cumulative thermal potential energy in the neighborhood of the parking space based on the thermal field state vector, and to remove parking spaces whose cumulative thermal potential energy exceeds the safety threshold in order to generate a set of safe candidate parking spaces. The optimization decision module is used to determine the optimal target parking space from the candidate parking space set by minimizing the comprehensive cost function that includes spatiotemporal matching and power grid balance terms, based on the expected occupancy time and the power grid load vector. The scheduling interlock module is used to generate speed adjustment commands based on the expected arrival time difference between the vehicle and the mobile power supply unit to achieve time and space synchronization, and to generate power derating commands based on the pre-start signal of the lifting motor of the three-dimensional parking garage to achieve transient power interlock protection.

2. The shared mobile charging system according to claim 1, characterized in that, The passive docking unit is configured as a passive interface containing only electrical contacts and detection contacts; The passive docking units at each parking space in the charging operation layer are physically marked as phase A interface, phase B interface or phase C interface according to the external power grid line they are connected to. The static attributes stored in the environment modeling module include the spatial geometric coordinates of each parking space and the corresponding power grid phase attributes.

3. The shared mobile charging system according to claim 1, characterized in that, The static attributes stored in the environment modeling module also include the mechanical access depth cost; The mechanical access depth cost is configured to quantify the standard time cost for the automated parking unit to move a vehicle from a specific parking space and transport it to the exit. The mechanical access depth cost is calculated based on the horizontal rated speed, vertical rated speed, and fixed retrieval time of the vehicle handling mechanism of the automated parking garage unit.

4. A shared mobile charging system according to claim 1, characterized in that, The demand forecasting module calculates the estimated duration of use as follows: Obtain the current state of charge, target state of charge, rated total battery energy, and maximum allowable charging voltage of the vehicle to be charged; The smaller value between the rated output power of the mobile power supply unit and the maximum allowable charging power of the vehicle is determined as the effective charging power. The theoretical charging time is calculated based on the effective charging power, and a charging curve correction coefficient is introduced to perform nonlinear compensation on the theoretical charging time to obtain the expected occupancy time.

5. A shared mobile charging system according to claim 1, characterized in that, The specific logic for the thermal field screening module to calculate the cumulative thermal potential energy is as follows: Define the thermal impact neighborhood set of the target parking space, the thermal impact neighborhood set including adjacent parking spaces whose Euclidean distance from the target parking space is less than a preset value; Obtain the current temperature value of each adjacent parking space in the environment modeling module; Based on the principle of thermal superposition, the current temperature values ​​of all adjacent parking spaces are weighted and summed according to the thermal coupling coefficient and the distance attenuation factor, and then superimposed on the reference temperature value of the target parking space to obtain the cumulative thermal potential energy.

6. A shared mobile charging system according to claim 3, characterized in that, The comprehensive cost function running inside the optimization decision module includes a spatiotemporal matching cost term and a power grid balance cost term; The spatiotemporal matching cost term is configured to evaluate the degree of matching between the expected occupancy time and the mechanical access depth cost. When the ratio between the expected occupancy time and the mechanical access depth cost approaches a preset value, the spatiotemporal matching cost term approaches a minimum value. The optimization decision module calculates and outputs the parking space number with the minimum comprehensive cost function as the optimal target parking space.

7. A shared mobile charging system according to claim 6, characterized in that, The calculation logic for the power grid balancing cost item is as follows: The apparent power of the three-phase load of the current power grid is obtained based on the power grid load vector; Based on the grid phase attributes of the candidate parking spaces, predict the three-phase load value after the vehicle is connected to the candidate parking space; The variance of the predicted three-phase load value is calculated and used as the power grid balancing cost term, so as to minimize the dispersion of the three-phase load by minimizing the variance.

8. A shared mobile charging system according to claim 1, characterized in that, The specific method by which the scheduling interlock module performs the mechanical scheduling function is as follows: Calculate the estimated vehicle transport time from the automated parking garage unit to the optimal target parking space, and the estimated module transport time from the shuttle transport unit to the mobile power supply unit to the optimal target parking space. Calculate the difference between the estimated vehicle handling time and the estimated module movement time; When the difference exceeds the synchronization tolerance threshold, the speed adjustment command is generated to control the operating speed of the one expected to arrive earlier, so that the vehicle and the mobile power supply unit arrive synchronously within a preset time window.

9. A shared mobile charging system according to claim 1, characterized in that, The specific method by which the scheduling interlock module performs electrical protection functions is as follows: Continuously monitor the operating status of the lifting motor in the automated parking garage unit; When the motor pre-start signal is received, the estimated transient surge power required for motor start-up and the current power margin of the power grid are calculated. The power derating command is generated and sent to the mobile power supply unit in operation to forcibly reduce the output power of the mobile power supply unit to offset the transient power surge generated by the motor starting, until the lifting motor is detected to have entered steady-state operation.

10. A shared mobile charging method, applied to the system as described in any one of claims 1 to 9, characterized in that, Includes the following steps: S10 establishes a digital mapping of the charging operation layer, defines the spatial coordinates of each parking space, the grid phase and the mechanical access depth cost, and updates the grid load vector and thermal field state vector in real time. S20: Read the battery parameters of the vehicle to be charged, and calculate the estimated occupancy time of the vehicle based on the battery parameters and the rated output power of the mobile power supply unit; S30, calculate the cumulative thermal potential energy of each parking space neighborhood in the charging operation layer, remove parking spaces that do not meet the thermal safety threshold, and generate a candidate parking space set; S40, construct a comprehensive cost function that includes a spatiotemporal matching cost term and a power grid balance cost term, calculate the cost value of each parking space in the candidate parking space set, and select the parking space with the smallest comprehensive cost value as the optimal target parking space; S50, calculate the estimated time difference between the vehicle and the mobile power supply unit arriving at the optimal target parking space, and generate a speed adjustment command to drive the control execution module so that the vehicle and the mobile power supply unit arrive synchronously. The S60 monitors the status of the lifting motor of the automated parking system during charging and generates a power derating command to limit the output power of the mobile power supply unit when a pre-start signal of the motor is detected.

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