Scheduling method of semi-matrix bridging full-flexible charging pile

Abstract

The invention relates to a scheduling method for a semi-matrix bridging full-flexible charging pile, and the method comprises the steps: obtaining the type of a to-be-charged vehicle and a charging condition needed by charging, obtaining the current state of the semi-matrix bridging full-flexible charging pile, obtaining the state of a parking space in front of a charging pile corresponding to the charging pile, and carrying out the scheduling of the charging pile. Establishing a system model according to the type of the to-be-charged vehicle, the required charging condition and the current state of the charging pile, matching a scheduling algorithm, setting a priority scheduling strategy according to the user demand, the electric quantity of the vehicle and the charging time, dynamically adjusting the scheduling sequence of the charging pile, and matching the corresponding charging pile and the charging parking space for the to-be-charged vehicle. The system can reasonably allocate charging pile resources and avoid resource waste by acquiring the type and charging conditions of a to-be-charged vehicle, can quickly match charging demands and charging resources and reduce vehicle waiting time by acquiring the charging pile state and the parking space state in real time, and can set priorities according to user demands, vehicle electric quantity and charging time, thereby improving the charging efficiency. The system can dynamically adjust the scheduling sequence, and it is ensured that charging services are preferentially provided for high-demand vehicles.

Description

Technical Field

[0001] The present application relates to charging pile scheduling technology, and in particular to a scheduling method for a fully flexible charging pile. Background Art

[0002] With the rapid development and popularization of new energy vehicles, the requirements for public charging equipment are increasing. At present, charging piles are mainly one pile with two guns, and the power of charging piles depends on different types. AC charging piles are generally 7KW, while the power of DC charging piles can be 15, 20, 30, 45, 60, 100, 150, 200, 250, 300KW. Now most DC charging piles use modular combined power, which can flexibly combine the power of charging piles according to user needs.

[0003] Usually, charging stations are equipped with slow charging piles, fast charging piles and ultra-fast charging piles. The power of slow charging piles is usually between 3kW and 22kW, which is suitable for places where the vehicle is parked at night or for a long time; the power of fast charging piles is generally between 30kW and 150kW, which is suitable for vehicles that need to be charged quickly in a short time; the power of ultra-fast charging piles can reach 200kW and above, and some can even reach 400kW, which is mainly used for electric vehicles that need to replenish power quickly, such as electric buses and large logistics vehicles. Existing scheduling systems usually do not support dynamic adjustment and cannot adapt to the charging needs of different types of electric vehicles. For example, the charging needs of high-power electric vehicles and low-power electric vehicles are quite different, and the lack of a flexible scheduling mechanism will lead to waste of resources. Therefore, there is an urgent need for an efficient scheduling method to improve charging efficiency and maintain stability. Summary of the invention

[0004] The present application provides a scheduling method for a semi-matrix bridged fully flexible charging stack and a dishwasher using the following technical solutions:

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

[0006] S120 obtains the current status of the half-matrix bridge fully flexible charging stack;

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

[0008] According to 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. A priority scheduling strategy is set according to user needs, vehicle power and charging time, and the scheduling order of the charging pile is dynamically adjusted to match the corresponding charging pile and charging parking space for the vehicle to be charged.

[0009] Optionally, the establishing of the system model includes establishing a charging stack model and a half-matrix bridge model, and the charging stack model is:

[0010]

[0011] Where: P is the charging power: defines the maximum power output of the charging stack;

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

[0013] C is the battery capacity: the vehicle to be charged

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

[0015]

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

[0017] I is the current, which indicates the amount of charge flowing in the circuit;

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

[0019] L is the inductance, which indicates the ability of the inductor element in the circuit to respond to changes in current;

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

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

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

[0023] The real-time scheduling is: combining real-time data, updating the charging strategy through dynamic planning, and ensuring the flexibility of the charging process.

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

[0025] Optionally, the setting of the priority scheduling strategy includes setting the priority according to the power of each vehicle, the expected charging time and the user demand:

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

[0027] Among them, SOCi is the battery status of the i-th vehicle, Di is the charging demand, Ti is the expected charging time, and w1, w_2, w_3 are weight coefficients.

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

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

[0030] 1. Obtain the type of vehicle to be charged, the charging conditions required for charging, the current state of the semi-matrix bridge fully flexible charging stack, and the parking space status in front of the charging pile corresponding to the charging stack. According to the type of vehicle to be charged, the required charging conditions and the current state of the charging stack, a system model is established, and a scheduling algorithm is matched. The priority scheduling strategy is set according to user needs, vehicle power and charging time, and the scheduling order of the charging stack is dynamically adjusted to match the corresponding charging pile and charging parking space for the vehicle to be charged. By obtaining the type of vehicle to be charged and the charging conditions, the system can reasonably allocate charging stack resources to avoid resource waste. By obtaining the status of the charging stack and the status of the parking space in real time, the charging demand and the charging resource can be quickly matched to reduce the waiting time of the vehicle. The priority is set according to user needs, vehicle power and charging time. The system can dynamically adjust the scheduling order to ensure that charging services are provided to high-demand vehicles first. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a schematic diagram of the process flow of an embodiment of the present application;

[0032] Figure 2 This is a schematic diagram of a single charging pile with two parking spaces in an embodiment of the present application;

[0033] Figure 3 This is a schematic diagram of a single charging pile and a single parking space in an embodiment of the present application. DETAILED DESCRIPTION

[0034] The following is combined with Figure 1-3 This application is described in further detail.

[0035] The embodiment of the present application discloses a scheduling method for a semi-matrix bridged fully flexible charging stack.

[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 vehicle, and obtain its specific brand and model in order to match the corresponding charging standard. Second, confirm the charging interface type (such as CCS or CHAdeMO) and the required charging power (slow charging or fast charging), and understand the current power status of the vehicle to determine the urgency of charging. In addition, it is necessary to obtain the user's charging time requirements and preferences for charging costs. This information will provide an important basis for subsequent scheduling and resource allocation.

[0038] S120 obtains the current status of the semi-matrix bridge fully flexible charging stack; including real-time monitoring of the operating status of the charging stack to ensure its normal operation and timely acquisition of fault alarm information. In addition, it is necessary to obtain the power output of the charging stack, including the current output voltage and current, to ensure that the charging needs of different vehicles are met. At the same time, check the connection status between the charging pile and the vehicle to be charged to confirm the stability and effectiveness of the connection. It is also necessary to determine the current charging mode (such as fast charging or slow charging) used by the charging stack and the charging standards it supports, and finally evaluate the availability of the charging stack, including the number of vehicles being served and the number of charging piles available for other vehicles. This information will provide important 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 pile;

[0040] This includes real-time monitoring of parking space occupancy to understand the number of currently available parking spaces. At the same time, it is necessary to confirm the type of parking space (such as dedicated electric vehicle charging space or ordinary parking space) in order to allocate resources reasonably. In addition, obtaining information on the use time of the parking space can help determine the expected idle time, thereby enabling effective scheduling. It is also important to regularly update parking space status information to ensure the real-time and accuracy of system data. At the same time, abnormal occupancy, such as temporary parking or illegal occupancy, should be identified and handled in a timely manner. These steps will provide a comprehensive understanding of the status of the parking space in front of the charging pile, providing strong support for charging scheduling and resource allocation.

[0041] According to 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. A priority scheduling strategy is set according to user needs, vehicle power and charging time, and the scheduling order of the charging pile is dynamically adjusted to match the corresponding charging pile and charging parking space for the vehicle to be charged.

[0042] Establishing the system model includes establishing a charging stack model and a half-matrix bridge model, wherein the charging stack model is:

[0043]

[0044] Where: P is the charging power: defines the maximum power output of the charging stack;

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

[0046] C is the battery capacity: for the vehicle to be charged.

[0047] It is understandable that there is an electric car with a battery capacity of C = 60kWh and a maximum power output of the charging stack of P = 22kW. According to the above formula, the charging time can be calculated:

[0048]

[0049] This means that at the charging stack's maximum power output, it will take about 2.73 hours to fully charge this EV's battery.

[0050] The semi-matrix bridge model includes the power grid model and the bridge circuit model:

[0051]

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

[0053] I is the current, which indicates the amount of charge flowing in the circuit;

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

[0055] L is the inductance, which indicates the ability of the inductor element in the circuit to respond to changes in current;

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

[0057] It is understandable that in a charging system, there are 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 passes through a resistor can be calculated:

[0063] P=V×I=400V×30A=12000W=12kW

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

[0065] It can be explained that, assuming there are three vehicles to be charged and two charging piles, the specific information is as follows:

[0066] Vehicle information to be charged

[0067] Vehicle A:

[0068] Battery capacity C = 60 kWh C = 60\text{kWh} C = 60 kWh

[0069] Current power = 10% (remaining power 6kWh)

[0070] Charging time required = 2 hours

[0071] Vehicle B:

[0072] Battery capacity C = 50 kWh C = 50\text{kWh} C = 50 kWh

[0073] Current power = 20% (remaining power 10kWh)

[0074] Charging time required = 1.5 hours

[0075] Vehicle C:

[0076] Battery capacity C = 70 kWh C = 70\text{kWh} C = 70 kWh

[0077] Current power = 50% (remaining power 35kWh)

[0078] Charging time required = 3 hours

[0079] Charging station information

[0080] Charging pile 1: Maximum power P = 22kWP

[0081] Charging pile 2: Maximum power P = 11kWP

[0082] Scheduling steps,

[0083] Data collection

[0084] The system monitors each vehicle’s power level, charging requirements, and charging station status in real time.

[0085] Priority Setting

[0086] ·Set priorities based on current power and charging needs:

[0087] o Vehicle A (priority 1, low battery, time is short)

[0088] o Vehicle B (priority 2, moderate battery, short time)

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

[0090] Dynamic Scheduling Algorithm

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

[0092] Matching charging pile

[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 11 kW. Calculate the charging time:

[0096]

[0097] Vehicle C is not 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 adjustments

[0101] If the charging time of vehicle A is detected to be prolonged (such as current fluctuation) during charging, the system will automatically adjust the schedule and may arrange charging for vehicle C in advance.

[0102] Simulation, establish a simulation platform based on MATLAB / Simulink, and simulate the scheduling process of multiple electric vehicles under different charging requirements. First, create a model structure, including electric vehicles, charging piles, scheduling control and control center modules. The electric vehicle module simulates the charging demand and power status, and the charging pile module manages the power output and status. The scheduling control module implements the scheduling algorithm through MATLAB Function, and makes reasonable arrangements according to the needs of electric vehicles and the availability of charging piles. The control center module collects the charging status of each vehicle and provides feedback to optimize the scheduling strategy. By setting the simulation time, running the model and observing the charging status and scheduling results, efficient charging management and resource utilization can be achieved.

[0103] The priority scheduling strategy includes setting the priority according to the power of each vehicle, the expected charging time and the user's needs:

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

[0105] Among them, SOCi is the battery status of the i-th vehicle, Di is the charging demand, Ti is the expected charging time, and w1, w_2, w_3 are weight coefficients.

[0106] It can be understood that there are three electric vehicles (A, B, C), and their battery status, charging demand and estimated charging time are: vehicle A (15% battery, high charging demand, estimated charging time of 2 hours), vehicle B (30% battery, medium charging demand, estimated charging time of 1.5 hours), vehicle C (60% battery, low charging demand, estimated charging time of 3 hours). Set the weight coefficient as

[0107] w1=0.5w_, w2=0.3w, w3=0.2w, and the priority of each vehicle is calculated according to the formula, and the priority of vehicle A is 8.8, vehicle B is 15.9, and vehicle C is 30.9. Finally, the system will allocate charging piles to vehicles C, B, and A according to the priority order, ensuring that vehicles with low power and high demand are charged first, thereby optimizing resource utilization and improving user satisfaction.

[0108] According to the output capacity of the charging pile and the power supply capacity of the power grid, the charging power is reasonably allocated to avoid overload and imbalance, and to ensure the efficient operation of the charging pile. In order to ensure the efficient operation of the charging pile and avoid overload and imbalance, it is necessary to reasonably allocate the charging power according to the output capacity of the charging pile and the power supply capacity of the power grid. First, the maximum output power of each charging pile is evaluated, and the power supply capacity of the power grid is monitored in real time. On this basis, the charging power is allocated to electric vehicles with high demand and low power through priority scheduling, and the charging power is dynamically adjusted to ensure that the total power does not exceed the maximum limit of the charging pile and the power grid. In addition, load balancing is required to evenly distribute power to multiple charging piles to avoid excessive load on one charging pile while other charging piles are idle. Finally, the charging strategy is adjusted in time through the monitoring and feedback system to optimize the charging process.

[0109] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A scheduling method for a semi-matrix bridged fully flexible charging stack, characterized in that: S110 obtains the type of vehicle to be charged and the charging conditions required for charging. S120 obtains the current status of the half-matrix bridge fully flexible charging stack; S130 obtains the parking space status in front of the charging pile corresponding to the charging pile; According to 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. A priority scheduling strategy is set according to user needs, vehicle power and charging time, and the scheduling order of the charging pile is dynamically adjusted to match the corresponding charging pile and charging parking space for the vehicle to be charged.

2. A scheduling method for a semi-matrix bridged fully flexible charging stack according to claim 1, characterized in that: The system model establishment includes establishing a charging stack model and a half-matrix bridge model, wherein the charging stack model is: Where: P is the charging power: defines the maximum power output of the charging stack; T is charging time: charging time calculated based on battery capacity and charging power; C is the battery capacity: for the vehicle to be charged.

3. A scheduling method for a semi-matrix bridged fully flexible charging stack according to claim 2, characterized in that: The semi-matrix bridge model includes a power grid model and a bridge circuit model: Where: V is the voltage, which represents the potential difference at a certain point in the circuit. I is the current, which indicates the amount of charge flowing in the circuit; R is resistance, which indicates the degree of resistance to the flow of current in the circuit; L is the inductance, which indicates the ability of the inductor element in the circuit to respond to changes in current; dt / dI is the rate of change of current, which indicates the rate at which current changes with time.

4. The method for scheduling a semi-matrix bridged fully flexible charging stack according to claim 1, characterized in that: The scheduling algorithm includes a heuristic algorithm and a real-time scheduling, and the heuristic algorithm is: Use heuristic methods such as genetic algorithms and particle swarm optimization to quickly find the optimal scheduling solution; The real-time scheduling is: combining real-time data, updating the charging strategy through dynamic planning, and ensuring the flexibility of the charging process.

5. The scheduling method of a semi-matrix bridged fully flexible charging stack according to claim 1, characterized in that: It also includes simulation, establishing a simulation platform based on MATLAB / Simulink to simulate the scheduling process of multiple electric vehicles under different charging requirements.

6. The method for scheduling a semi-matrix bridged fully flexible charging stack according to claim 1, characterized in that: The priority scheduling strategy includes setting the priority according to the power of each vehicle, the expected charging time and the user's needs: Priority i =w1·SOC i +w2·T i +w3·D i Among them, SOCi is the battery status of the i-th vehicle, Di is the charging demand, Ti is the expected charging time, and w1, w_2, w_3 are weight coefficients.

7. The method for scheduling a semi-matrix bridged fully flexible charging stack according to claim 1, characterized in that: It also includes reasonably allocating charging power according to the output capacity of the charging stack and the power supply capacity of the power grid to avoid overload and imbalance and ensure the efficient operation of the charging stack.

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