A dispatching method for a semi-matrix bridge full-flexible charging pile

Abstract

The application relates to a scheduling method of a semi-matrix bridging full-flexible charging pile, acquires a type of a vehicle to be charged, acquires charging conditions required by charging, acquires a current state of a semi-matrix bridging full-flexible charging pile, acquires a parking space state in front of a charging pile corresponding to the charging pile, establishes a system model according to the type of the vehicle to be charged, the required charging conditions and the current state of the charging pile, matches a scheduling algorithm, sets a priority scheduling strategy according to user demand, vehicle power and charging time, dynamically adjusts a scheduling sequence of the charging pile, matches corresponding charging piles and charging parking spaces for the vehicle to be charged, and through acquisition of the type of the vehicle to be charged and the charging conditions, the system can reasonably allocate charging pile resources and avoid resource waste; through real-time acquisition of the state of the charging pile and the state of the parking space, the charging demand and charging resources can be quickly matched, the waiting time of the vehicle is reduced, the priority is set according to the user demand, the vehicle power and the charging time, the system can dynamically adjust the scheduling sequence, and high-demand vehicles can be preferentially provided with charging services.

Description

Technical Field

[0001] This application relates to charging pile scheduling technology, and in particular to a scheduling method for fully flexible charging piles. Background Technology

[0002] With the rapid development and popularization of new energy vehicles, the requirements for public charging equipment are increasing. Currently, charging piles are mainly one pile with two charging guns, and the power of the charging pile varies depending on the type. AC charging piles are generally 7KW, while DC charging piles can have power ratings of 15, 20, 30, 45, 60, 100, 150, 200, 250, and 300KW. Most DC charging piles now use modular power combinations, allowing for flexible power configurations based on user needs.

[0003] Charging stations typically have slow charging stations, fast charging stations, and ultra-fast charging stations. Slow charging stations usually have a power output between 3kW and 22kW, suitable for nighttime charging or locations with long-term parking. Fast charging stations generally have a power output between 30kW and 150kW, suitable for vehicles requiring rapid charging in a short period. Ultra-fast charging stations can reach 200kW and above, with some even reaching 400kW, primarily used for electric vehicles requiring rapid recharging, such as electric buses and large logistics vehicles. Existing scheduling systems typically do not support dynamic adjustments and cannot adapt to the charging needs of different types of electric vehicles. For example, the charging needs of high-power electric vehicles differ significantly from those of low-power electric vehicles, and the lack of a flexible scheduling mechanism leads to resource waste. Therefore, an efficient scheduling method is urgently needed to improve charging efficiency and maintain stability. Summary of the Invention

[0004] This application provides a scheduling method for a semi-matrix bridged fully flexible charging pile and a dishwasher, which adopts the following technical solution:

[0005] A scheduling method for a semi-matrix bridged fully flexible charging pile, wherein S110 obtains the type of vehicle to be charged and the charging conditions required for charging.

[0006] S120 acquires the current state of the semi-matrix bridged fully flexible charging stack;

[0007] S130 obtains the parking space status in front of the charging pile corresponding to the charging stack;

[0008] Based on the type of vehicle to be charged, the required charging conditions, and the current state of the charging pile, a system model is established and a scheduling algorithm is matched. Priority scheduling strategies are set according to user needs, vehicle battery level, and charging time, and the scheduling order of the charging piles is dynamically adjusted to match the corresponding charging piles and charging spaces for the vehicles to be charged.

[0009] Optionally, establishing the system model includes establishing a charging pile model and a semi-matrix bridging model, wherein the charging pile model is:

[0010]

[0011] Where: P is the charging power: defining the maximum power output of the charging pile;

[0012] T represents the charging time: the charging time calculated based on the battery capacity and charging power;

[0013] C represents the battery capacity: for the vehicle to be charged.

[0014] Optionally, the semi-matrix bridging model includes a power grid model and a bridging circuit model:

[0015]

[0016] Where: V is voltage, representing the potential difference at a certain point in the circuit.

[0017] I represents the current, indicating the amount of charge flowing in the circuit;

[0018] R is the resistance, which represents the degree of obstruction to the flow of current in a circuit;

[0019] L stands for inductance, representing the ability of an inductive element in a circuit to respond to changes in current.

[0020] dt / dI is the rate of change of current, representing the rate at which the current changes with time.

[0021] Optionally, the scheduling algorithm includes a heuristic algorithm and real-time scheduling, wherein the heuristic algorithm is:

[0022] Use heuristic methods such as genetic algorithms and particle swarm optimization to quickly find the optimal scheduling scheme;

[0023] The real-time scheduling involves combining real-time data with dynamic planning to update the charging strategy, ensuring the flexibility of the charging process.

[0024] Optionally, simulation can also be included, establishing a simulation platform based on MATLAB / Simulink to simulate the scheduling process of multiple electric vehicles under different charging demands.

[0025] Optionally, the priority scheduling strategy includes priority setting, which sets priorities based on the battery level of each vehicle, the estimated charging time, and user demand.

[0026] Priority i =w1·SOC i +w2·T i +w3·D i

[0027] Where SOCi is the battery state of the i-th vehicle, Di is the charging requirement, Ti is the estimated charging time, and w1, w_2, w_3 are weighting coefficients.

[0028] Optionally, it also includes rationally allocating charging power according to the output capacity of the charging pile and the power supply capacity of the grid to avoid overload and imbalance, and ensure the efficient operation of the charging pile.

[0029] In summary, this application includes at least one of the following beneficial technical effects:

[0030] 1. Obtain the type of vehicle to be charged and the charging conditions required for charging, obtain the current state of the semi-matrix bridged fully flexible charging pile, and obtain the parking space status in front of the corresponding charging pile. Based on the type of vehicle to be charged, the required charging conditions, and the current state of the charging pile, establish a system model and match a scheduling algorithm. Set a priority scheduling strategy according to user demand, vehicle battery level, and charging time, and dynamically adjust the scheduling order of the charging piles to match the corresponding charging piles and charging parking spaces for the vehicles to be charged. By obtaining the type of vehicle to be charged and the charging conditions, the system can reasonably allocate charging pile resources and avoid resource waste. By obtaining the charging pile status and parking space status in real time, it can quickly match charging demand with charging resources, reduce vehicle waiting time, and set priorities according to user demand, vehicle battery level, and charging time. The system can dynamically adjust the scheduling order to ensure that charging services are provided to vehicles with high demand first. Attached Figure Description

[0031] Figure 1 This is a flowchart illustrating an embodiment of this application;

[0032] Figure 2 This is a schematic diagram of a single charging pile with two parking spaces according to an embodiment of this application;

[0033] Figure 3 This is a schematic diagram of a single charging pile and a single parking space in an embodiment of this application. Detailed Implementation

[0034] The following is in conjunction with the appendix Figure 1-3 This application will be described in further detail.

[0035] This application discloses a scheduling method for a semi-matrix bridged fully flexible charging pile.

[0036] S110 obtains the type of vehicle to be charged and the charging conditions required for charging.

[0037] First, identify the type of vehicle to be charged, such as an electric vehicle or a plug-in hybrid, and obtain its specific brand and model to match the appropriate charging standard. Second, confirm the charging interface type (e.g., CCS or CHAdeMO) and the required charging power (slow or fast charging), while also understanding the vehicle's current battery status to determine the urgency of charging. Furthermore, it is necessary to obtain the user's charging time requirements and charging cost preferences; this information will provide crucial data for subsequent scheduling and resource allocation.

[0038] The S120 acquires the current status of the semi-matrix bridged fully flexible charging pile, including real-time monitoring of its operation to ensure proper functioning and timely fault alarm information. Furthermore, it needs to acquire the charging pile's power output, including current output voltage and current, to ensure it meets the charging needs of different vehicles. Simultaneously, it checks the connection status between the charging pile and the vehicle being charged, confirming the stability and validity of the connection. It also needs to determine the charging mode currently used by the charging pile (e.g., fast charging or slow charging) and the supported charging standards. Finally, it assesses the charging pile's availability, including the number of vehicles currently being served and the number of charging piles available for other vehicles. This information will provide crucial data support for subsequent scheduling and resource allocation.

[0039] S130 obtains the parking space status in front of the charging pile corresponding to the charging stack;

[0040] This includes real-time monitoring of parking space occupancy to understand the number of available spaces. It's also necessary to identify the type of parking space (e.g., dedicated electric vehicle charging spots or regular parking spaces) to ensure rational resource allocation. Furthermore, obtaining parking space usage time information helps determine expected idle time, enabling effective scheduling. Regularly updating parking space status information to ensure the real-time nature and accuracy of system data is also crucial. Additionally, abnormal occupancy, such as temporary parking or illegal occupation, should be identified and addressed promptly. These steps provide a comprehensive understanding of the parking space status in front of charging stations, offering strong support for charging scheduling and resource allocation.

[0041] Based on the type of vehicle to be charged, the required charging conditions, and the current state of the charging pile, a system model is established and a scheduling algorithm is matched. Priority scheduling strategies are set according to user needs, vehicle battery level, and charging time, and the scheduling order of the charging piles is dynamically adjusted to match the corresponding charging piles and charging spaces for the vehicles to be charged.

[0042] The system model establishment includes establishing a charging pile model and a semi-matrix bridging model. The charging pile model is as follows:

[0043]

[0044] Where: P is the charging power: defining the maximum power output of the charging pile;

[0045] T represents the charging time: the charging time calculated based on the battery capacity and charging power;

[0046] C represents the battery capacity; represents the vehicle to be charged.

[0047] It is understandable that an electric vehicle has a battery capacity of C = 60 kWh and a charging pile with a maximum power output of P = 22 kW. Based on the above formula, the charging time can be calculated:

[0048]

[0049] This means that at the maximum power output of the charging pile, it would take approximately 2.73 hours to fully charge the battery of this electric vehicle.

[0050] The semi-matrix bridging model includes a power grid model and a bridging circuit model:

[0051]

[0052] Where: V is voltage, representing the potential difference at a certain point in the circuit.

[0053] I represents the current, indicating the amount of charge flowing in the circuit;

[0054] R is the resistance, which represents the degree of obstruction to the flow of current in a circuit;

[0055] L stands for inductance, representing the ability of an inductive element in a circuit to respond to changes in current.

[0056] dt / dI is the rate of change of current, representing the rate at which the current changes with time.

[0057] It is understandable that a charging system has the following parameters:

[0058] V = 400V (voltage)

[0059] I = 30A (current)

[0060] R = 13.33Ω (resistance)

[0061] L = 0.1H (inductance)

[0062] According to Ohm's law, the power loss when current flows through a resistor can be calculated:

[0063] P=V×I=400V×30A=12000W=12kW

[0064] Assuming the current changes during charging, if the rate of change of current is dt / dI = 0.5 A / s, this means that the current increases by 0.5 amperes per second.

[0065] To clarify, let's assume there are three vehicles waiting to be charged and two charging stations, with the following details:

[0066] Information on vehicles awaiting charging

[0067] Vehicle A:

[0068] Battery capacity C = 60kWh

[0069] Current battery level = 10% (remaining battery capacity 6kWh)

[0070] Charging time requirement = 2 hours

[0071] Vehicle B:

[0072] Battery capacity C = 50kWh

[0073] Current battery level = 20% (remaining battery capacity 10kWh)

[0074] Charging time required = 1.5 hours

[0075] Vehicle C:

[0076] Battery capacity C = 70kWh

[0077] Current battery level = 50% (remaining battery capacity 35kWh)

[0078] Charging time requirement = 3 hours

[0079] Charging station information

[0080] Charging station 1: Maximum power P = 22kWh

[0081] Charging station 2: Maximum power P = 11kWp

[0082] Scheduling steps,

[0083] Data collection

[0084] The system monitors the battery level, charging needs, and charging station status of each vehicle in real time.

[0085] Priority setting

[0086] • Set priorities based on current battery level and charging needs:

[0087] o Vehicle A (Priority 1, low battery, time is of the essence)

[0088] o Vehicle B (Priority 2, moderate battery level, short duration)

[0089] o Vehicle C (Priority 3, high battery level, long charging time)

[0090] Dynamic scheduling algorithm

[0091] Using a priority queue, vehicle A is first dispatched to charging station 1.

[0092] Matching charging stations

[0093] Vehicle A uses charging station 1 with a charging power of 22kW. Calculate the charging time:

[0094]

[0095] Vehicle B uses charging station 2 with a charging power of 11kW. Calculate the charging time:

[0096]

[0097] Vehicle C will not be dispatched for the time being.

[0098] Execution scheduling

[0099] Vehicle A starts charging, and vehicle B starts charging after vehicle A has finished charging.

[0100] Real-time feedback and adjustment

[0101] If the charging time of vehicle A is detected to be prolonged (e.g., due to current fluctuations) during the charging process, the system will automatically adjust the scheduling and may arrange charging for vehicle C in advance.

[0102] Simulation was conducted using a MATLAB / Simulink-based simulation platform to model the scheduling process of multiple electric vehicles under varying charging demands. First, a model structure was created, including electric vehicles, charging stations, a scheduling control module, and a control center module. The electric vehicle module simulates charging demand and battery status, while the charging station module manages power output and status. The scheduling control module implements a scheduling algorithm using MATLAB Functions to rationally allocate resources based on electric vehicle demand and charging station availability. The control center module collects and provides feedback on the charging status of each vehicle to optimize the scheduling strategy. By setting a simulation time, running the model, and observing the charging status and scheduling results, efficient charging management and resource utilization are achieved.

[0103] The priority scheduling strategy includes priority setting, which sets priorities based on the battery level of each vehicle, the estimated charging time, and user demand.

[0104] Priority i =w1·SOC i +w2·T i +w3·Di

[0105] Where SOCi is the battery state of the i-th vehicle, Di is the charging requirement, Ti is the estimated charging time, and w1, w_2, w_3 are weighting coefficients.

[0106] Assume there are three electric vehicles (A, B, and C) with the following battery status, charging needs, and estimated charging times: Vehicle A (15% battery, high charging need, estimated charging time 2 hours), Vehicle B (30% battery, medium charging need, estimated charging time 1.5 hours), and Vehicle C (60% battery, low charging need, estimated charging time 3 hours). Let the weighting coefficients be...

[0107] Given w1 = 0.5w, w2 = 0.3w, and w3 = 0.2w, the priority of each vehicle is calculated using the formula. Vehicle A has a priority of 8.8, vehicle B 15.9, and vehicle C 30.9. Ultimately, the system will allocate charging stations to vehicles C, B, and A according to their priority order, ensuring that vehicles with low battery levels and high demand are charged first, thereby optimizing resource utilization and improving user satisfaction.

[0108] Based on the output capacity of the charging piles and the power supply capacity of the power grid, charging power is rationally allocated to avoid overload and imbalance, ensuring the efficient operation of the charging piles. To ensure efficient operation and avoid overload and imbalance, charging power needs to be rationally allocated according to the output capacity of the charging piles and the power supply capacity of the power grid. First, the maximum output power of each charging pile is assessed, and the power supply capacity of the power grid is monitored in real time. Based on this, charging power is allocated to electric vehicles with high demand and low battery levels through priority scheduling, while dynamically adjusting the charging power to ensure that the total power does not exceed the maximum limits of the charging piles and the power grid. Furthermore, load balancing needs to be achieved, distributing power evenly across multiple charging piles to prevent one charging pile from being overloaded while others are idle. Finally, through a monitoring and feedback system, the charging strategy is adjusted in a timely manner to optimize the charging process.

[0109] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A scheduling method for a semi-matrix bridging full-flexible charging pile, characterized in that: S110 obtaining the type of a vehicle to be charged and the charging condition required for charging, S120 obtaining the current state of the semi-matrix bridging full-flexible charging pile, S130 obtaining the state of a parking space in front of a charging pile corresponding to the charging pile, and according to the type of the vehicle to be charged, the charging condition required for charging, and the current state of the charging pile, a system model is established, a scheduling algorithm is matched, a priority scheduling strategy is set according to user demand, vehicle power, and charging time, the scheduling order of the charging pile is dynamically adjusted, a corresponding charging pile and a charging parking space are matched for the vehicle to be charged, the system model is established by establishing a charging pile model and a semi-matrix bridging model, the charging pile model is: wherein P is the charging power, which defines the maximum power output of the charging pile, T is the charging time, which is calculated according to the battery capacity and the charging power, and C is the battery capacity, which is the vehicle to be charged, the semi-matrix bridging model includes a power grid model and a bridging circuit model: wherein V is the voltage, which represents the potential difference at a certain point in the circuit, I is the current, which represents the amount of charge flowing in the circuit, R is the resistance, which represents the degree of hindrance to the flow of current in the circuit, L is the inductance, which represents the response ability of the inductive element in the circuit to the change of current, and dl / dt is the rate of change of current, which represents the rate of change of current with time, the scheduling algorithm includes a heuristic algorithm and a real-time scheduling, the heuristic algorithm is: using a genetic algorithm and a particle swarm optimization heuristic method to quickly find an optimal scheduling scheme, the real-time scheduling is: combining real-time data to update the charging strategy through dynamic programming to ensure the flexibility of the charging process, simulation is further included, a simulation platform based on MATLAB / Simulink is established to simulate the scheduling process of multiple electric vehicles under different charging demands, the setting of the priority scheduling strategy includes priority setting, which is set according to the power of each vehicle, the estimated charging time, and user demand, and further includes reasonably allocating the charging power according to the output capacity of the charging pile and the power supply capacity of the power grid. ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ ​ 2. The scheduling method of a semi-matrix-bridged full-flexible charging stack according to claim 1, characterized in that: ​ ​ ​ 3. The scheduling method of a semi-matrix-bridged full-flexible charging stack according to claim 1, characterized in that: ​ 4. The scheduling method of a semi-matrix-bridged full-flexible charging stack according to claim 1, characterized in that: ​ Priority i = w1 · SOC i + w2 · T i + w3 · D i where SOC i is the state of charge of the i-th vehicle, D i is the charging demand, T i is the predicted charging time, and w1, w2, w3 are weight coefficients.

5. The scheduling method of a semi-matrix-bridged full-flexibility charging stack according to claim 1, characterized in that: ​

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