Scheduling method and system for orderly charging of electric vehicles in old community

Abstract

The invention discloses an orderly charging scheduling method and system for electric vehicles in an old community, and the method comprises the steps: obtaining the charging demand data of the electric vehicles in a charging station, calculating and dividing the charging priority according to the charging demand data, and constructing a charging prediction model; cable temperature data are collected, whether an overheating risk exists or not is judged, and charging is suspended if the overheating risk exists; if the overheating risk does not exist, calculating estimated total charging power of the charging station according to a charging prediction model, and obtaining a transformer load rate of a power grid where the charging station is located according to the estimated total charging power of the charging station; and making a charging decision according to the user priority and the load rate of the power grid transformer of the charging station. According to the invention, self-adaptive matching between the charging demand and the power grid bearing capacity can be realized, charging resources are distributed in order, the peak load of the power distribution network is relieved, and the power utilization safety and the scheduling efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of charging scheduling, and more specifically, to a method and system for orderly charging scheduling of electric vehicles in old communities. Background Art

[0002] With the deepening global understanding of sustainable development and the significant rise in environmental awareness, electric vehicles, as an innovative and environmentally friendly mode of transportation, are gaining popularity at an unprecedented rate. This trend not only reflects the international community's urgent need to mitigate climate change, reduce greenhouse gas emissions, and protect the natural environment, but also demonstrates the integration of scientific and technological progress with environmental protection.

[0003] However, the power infrastructure in older communities is often not designed for large-scale electric vehicle charging needs. Faced with growing charging demand and due to the lack of effective charging scheduling strategies, the grid transformers and lines in older communities may not be able to withstand the load of a large number of electric vehicles charging at the same time, which can easily lead to overloads and cause power supply failures and other problems.

[0004] Therefore, when charging electric vehicles, the power infrastructure of existing old communities is often not designed for large-scale electric vehicle charging needs, and it is difficult to meet the growing charging demand. In addition, although there are methods in the existing technology that combine multiple factors to construct charging scheduling strategies, the classification of charging user levels in the existing technology mostly relies on subjective settings or simple parameter calculations, which is not reasonable enough. In addition, the adjustment of the target charging power is only based on the grid load, without taking multiple factors into consideration, resulting in charging scheduling that is not flexible and accurate enough. Summary of the Invention

[0005] In order to address the deficiencies in the prior art, the present invention provides a method for orderly charging scheduling of electric vehicles in old communities, which can solve the problem in the prior art that the power infrastructure in old communities lacks a scheduling method for large-scale electric vehicle charging needs.

[0006] The present invention adopts the following technical solutions.

[0007] A method for orderly charging and scheduling electric vehicles in old communities, comprising the following steps:

[0008] Obtain charging demand data for electric vehicles in charging stations, calculate and prioritize charging based on the charging demand data, and build a charging prediction model;

[0009] Collect cable temperature data and determine whether there is an overheating risk. If there is an overheating risk, charging will be suspended;

[0010] If there is no overheating risk, the estimated total charging power of the charging station is calculated based on the charging prediction model, and the transformer load factor of the power grid where the charging station is located is obtained based on the estimated total charging power of the charging station.

[0011] Charging decisions are made based on user priority and charging station grid transformer load rate.

[0012] Preferably, the charging requirement data includes charging power requirement, charging start time, charging duration, and battery remaining capacity.

[0013] Preferably, the calculation formula of the charging priority is:

[0014]

[0015] Where λ i The charging priority of the electric vehicle currently connected to the charging pile i is [0-1], SOC i D is the remaining battery power of the electric vehicle currently connected to charging pile i; i D is the predicted remaining mileage requirement of the electric vehicle currently connected to charging pile i; max is the maximum mileage of the electric vehicle; N i N is the historical cumulative charging times of the electric vehicle currently connected to the charging pile i; max is the maximum value of the cumulative charging times of the electric vehicle; ∝, β, γ, δ are preset weight coefficients, and satisfy ∝+β+γ+δ=1; T i is the waiting time for charging of the electric vehicle currently connected to the charging pile i; f(SOC i ,T i ) is the interaction factor function of the electric vehicle currently connected to charging pile i; k is the dynamic weight parameter;

[0016] The charging priority is divided as follows:

[0017] Setting a first priority threshold and a second priority threshold for charging priority;

[0018] If λ i If the value is less than the first priority threshold, it means that the charging priority of the electric vehicle is low;

[0019] If λ i If the value is greater than or equal to the first priority threshold and less than the second priority threshold, it means that the charging priority of the electric vehicle is medium priority;

[0020] If λ i If the value is greater than the second priority threshold, it indicates that the charging priority of the electric vehicle is high.

[0021] Preferably, the charging prediction model is:

[0022]

[0023] Among them, P pred (t) is the estimated total charging power of the charging station at time t; P i (t) is the charging power demand of the electric vehicle currently connected to charging pile i at time t, and N is the total number of charging piles in the charging station; f(t,T i ,τ i ,D i ) is the indicator function, T i is the time when the electric vehicle starts charging when the charging pile i is currently connected, τ i is the charging duration of the electric vehicle currently connected to charging pile i, D i is the battery capacity of the electric vehicle currently connected to charging pile i at time t, and its value range is [0,1].

[0024] Preferably, the exponential function f(t,T i ,τ i ,D i The specific situation is as follows:

[0025] If the remaining mileage demand is predicted to be D i =0, indicating that the battery of the electric vehicle currently connected to the charging pile i is fully charged at time t. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0;

[0026] If the remaining mileage demand is predicted to be D i is greater than 0 and less than the initial remaining mileage requirement, indicating that the battery of the electric vehicle currently connected to the charging pile i is not fully charged at time t, and T i ≤t≤T i +τ i , which means that at time t, the charging pile i is currently connected to the electric vehicle and is in the charging state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 1;

[0027] If the remaining mileage demand is predicted to be D i is equal to the initial remaining mileage requirement, and T i >t, indicating that at time t, the electric vehicle currently connected to the charging pile i is in an uncharged state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0;

[0028] The initial remaining mileage demand is the predicted remaining mileage demand when the electric vehicle starts charging.

[0029] Preferably, if the battery of the electric vehicle is not full, and the exponential function f(t,T i ,τ i ,D i )=1, then construct an exponential adjustment function to dynamically adjust the target charging power, and the adjusted target charging power The charging power demand of the electric vehicle currently connected to the charging pile i at time t is used for the calculation of the charging prediction model;

[0030] The constructed exponential adjustment function is as follows:

[0031]

[0032] Where, is the adjusted target charging power; is the regulation coefficient, reflecting the power reduction strength when the load increases; L(t) is the grid transformer load at the current moment; L max is the maximum rated load of the grid transformer.

[0033] Preferably, the calculation formula for the transformer load rate of the power grid where the charging station is located is:

[0034]

[0035] Among them, S t is the transformer load factor at time t, P max is the rated maximum load of the transformer.

[0036] Preferably, the specific circumstances of making charging decisions based on user priority and charging station grid transformer load rate are as follows:

[0037] Setting a first limit value and a second limit value, so that the first limit value is less than the second limit value;

[0038] If the transformer load rate of the power grid where the charging station is located is less than the first limit value, it indicates a low-load period, and all priority users are allowed to charge;

[0039] If the transformer load rate of the power grid where the charging station is located is greater than or equal to the first limit value and less than or equal to the second limit value, it indicates a medium load period, and high-priority and medium-priority users are allowed to charge;

[0040] If the transformer load rate of the power grid where the charging station is located is greater than the second limit value, it indicates a high-load period, and high-priority charging is allowed.

[0041] The present invention also proposes a system for orderly charging and scheduling of electric vehicles in old communities, which is used in the method for orderly charging and scheduling of electric vehicles in old communities, comprising:

[0042] The data acquisition module is used to obtain the charging demand data of electric vehicles in the charging station, collect cable temperature data, and calculate and divide the charging priority according to the charging demand data;

[0043] A model building module is used to build a charging prediction model based on charging demand data;

[0044] A prediction module is used to calculate the estimated total charging power of the charging station based on the charging prediction model, and obtain the transformer load rate of the power grid where the charging station is located based on the estimated total charging power of the charging station;

[0045] The decision-making module is used to determine whether there is an overheating risk based on cable temperature data, and to make charging decisions based on user priority and the load rate of the charging station grid transformer.

[0046] The present invention also provides a terminal, comprising a processor and a storage medium;

[0047] The storage medium is used to store instructions;

[0048] The processor is used to operate according to the instructions to execute the steps of the method for orderly charging scheduling of electric vehicles in old communities.

[0049] The present invention also proposes a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps of the method for orderly charging scheduling of electric vehicles in old communities are implemented.

[0050] The beneficial effects of the present invention are that, compared with the prior art, the present invention has at least the following beneficial effects:

[0051] 1. When processing user charging needs, this invention not only considers the remaining battery power, but also introduces interactive factors such as mileage requirements, historical charging behavior, and waiting time. Through the Logistic function, it adapts to real-time loads and effectively balances the scheduling strategy between peak and off-peak periods, ensuring that the charging needs of different users are reasonably met, improving scheduling fairness and user experience.

[0052] 2. Based on the constructed charging prediction model, the present invention dynamically constrains the target charging power by constructing an exponential adjustment function. When the grid load approaches the rated value, the power is automatically reduced to participate in the total power aggregation, achieving peak reduction and valley filling. This intelligent adjustment can be performed without manual intervention, significantly reducing the risk of grid load fluctuations.

[0053] 3. By incorporating cable temperature sensing and transformer load factor calculation into scheduling decisions, the present invention can automatically stop or release charging in stages when overheating or high load is detected, finely manage charging permissions for users of different priorities, effectively prevent line overheating and transformer overload, and improve system safety and stability.

[0054] 4. This invention modularizes functions such as data collection, model building, prediction and decision-making. Each module has clear responsibilities and can be flexibly integrated into the charging infrastructure of different old communities. It supports docking with multiple interfaces such as cloud platforms and mobile terminals, and has good scalability and maintainability.

[0055] 5. Through precise demand forecasting and priority scheduling, an exponential adjustment function is constructed to dynamically constrain and adjust the target charging power. This invention can smoothly distribute the charging load, maximize the use of idle capacity in the power grid, and reduce waiting time during peak hours. This not only improves the utilization rate of transformers and lines, but also reduces the burden of waiting for users to charge. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a flow chart of the method for orderly charging and scheduling electric vehicles in old communities in the present invention;

[0057] Figure 2 It is a structural diagram of the orderly charging scheduling system for electric vehicles in old communities in the present invention. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without making creative efforts are all within the scope of protection of the present invention.

[0059] Example 1

[0060] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for orderly charging scheduling of electric vehicles in old communities, including the following steps:

[0061] S1: Collect the charging demand of electric vehicles and pre-process them to obtain charging demand data, divide the charging priority according to the charging demand data, and build a charging prediction model based on the charging demand data.

[0062] The charging requirements submitted by the user are obtained through a smart charging terminal, mobile application or cloud platform. The charging requirement data includes charging power requirement, charging start time, charging duration, and remaining battery power. The preprocessing includes cleaning abnormal sensor data, normalizing cable temperature and other data.

[0063] S11: Calculate the charging priority of electric vehicles connected to each charging pile in the charging station, and prioritize users according to the charging priority;

[0064] The calculation of charging priority includes: calculating the proportion of power to be charged, mileage and charging times according to the remaining battery power, predicted remaining mileage demand, maximum mileage, historical charging times and the maximum cumulative charging times of the electric vehicles connected to each charging station in the charging station;

[0065] Construct an interaction factor function that reflects the load condition, set the proportion of power to be charged, mileage and charging times, and the weight coefficient of the interaction factor to calculate the comprehensive evaluation results of each electric vehicle;

[0066] Dynamic weight parameters are set to process the comprehensive evaluation results and obtain the charging priority score of the electric vehicle.

[0067] Furthermore, the calculation formula for charging priority is:

[0068]

[0069] Where λ i is the charging priority of the electric vehicle currently connected to the charging pile i, with a value range of [0,1], SOC i D is the remaining battery power of the electric vehicle currently connected to charging pile i, in percentage; i is the predicted remaining mileage demand of the electric vehicle currently connected to charging pile i, and the first predicted remaining mileage demand obtained is the initial remaining mileage demand; D max is the maximum mileage of the electric vehicle; N i N is the historical cumulative charging times of the electric vehicle currently connected to the charging pile i; max is the maximum value of the cumulative charging times of the electric vehicle; ∝, β, γ, δ are weight coefficients respectively, which are pre-set by those skilled in the art, and ∝+β+γ+δ=1; T i is the waiting time for charging of the electric vehicle currently connected to the charging pile i; f(SOC i ,T i ) is the interaction factor function of the electric vehicle currently connected to charging pile i, which is used to reflect the coupling effect of charge state and waiting time; k is the dynamic weight parameter;

[0070] It should be noted that the above charging priority formula is based on a scoring model that integrates multiple factors and is used to quantitatively judge the charging urgency of electric vehicle users in old communities, thereby providing a scientific basis for scheduling strategies. i Departure, reflecting the current charging urgency; further introduce the prediction of remaining mileage demand D i With maximum mileage D max to measure the endurance pressure; at the same time, the proportion of historical cumulative charging times is taken into account to reflect the user's long-term usage behavior and service fairness; combined with the waiting time T iAs an indicator of immediate demand intensity; finally, an interactive factor function and dynamic weight parameters are introduced to enable the priority value to be adjusted in real time according to the current charging station load status, thus taking into account fairness and scheduling flexibility in different scenarios. Moreover, each factor is linearly combined by setting weight coefficients, which not only ensures the simplicity of calculation but also objectively reflects the comprehensive impact of multi-dimensional conditions;

[0071] The specific circumstances of the priority classification of users according to charging priority are as follows:

[0072] If λ i If it is less than the first priority threshold, it is represented as low priority;

[0073] If λ i If the value is greater than or equal to the first priority threshold and less than the second priority threshold, it is considered medium priority.

[0074] If λ i If it is greater than the second priority threshold, it is indicated as high priority;

[0075] Furthermore, the interaction factor function f(SOC i ,T i ) and the dynamic weight parameter k satisfy:

[0076]

[0077] Where L(t) is the charging load rate in the current period, which is the ratio of the number of reserved vehicles to the total number of parking spaces in the charging station; L ref is the load rate reference threshold. When L(t) exceeds the load rate reference threshold, the dispatching strategy tilts toward the peak. min , k max are the upper and lower limits of parameter k, and its value is pre-set by those skilled in the art; η is the coefficient for adjusting the steepness, which can be set according to the sensitivity of the scheduling system to fluctuations; μ and λ are constant parameters, which can be set manually based on experiments;

[0078] It should be noted that the above interaction factor function comprehensively considers two key factors: the battery state of charge and the user's waiting time for charging. First, when the battery level is low and the waiting time is long, the user's demand for charging is often stronger; conversely, when the battery level is high or the waiting time is short, the user is willing to continue waiting in line. To this end, the interaction factor function uses normalization to map the battery state of charge and waiting time to the same dimension, and introduces a control parameter to set the critical load rate threshold. This allows the function slope and sensitivity to be adaptively adjusted according to the actual changes in the charging load, allowing the scheduling system to more reasonably prioritize high-demand users.

[0079] The above dynamic weight parameters take into account the impact of real-time changes in charging load. Specifically, the current charging load rate is used as input to adaptively adjust the calculation weight of user priority. Based on the response characteristics of the Logistic function, it can maintain a small weight when the load is low to avoid wasting resources; and quickly increase the weight when the load approaches the set threshold to give priority to users in urgent need of charging, reflecting the scheduling system's sensitivity to peak hours. At the same time, by setting the upper and lower limits of the function and the slope factor, the parameter change process is smooth and can be quickly adjusted at critical moments, ensuring that the scheduling strategy is flexible and real-time.

[0080] The present invention not only considers the state of charge, but also introduces mileage requirements, historical charging behavior, and the state of charge-waiting time interaction term to fully reflect the vehicle's power demand and fairness; the calculation of the parameter k can adapt to the real-time load and has both peak response and valley smooth scheduling capabilities.

[0081] S12: Construct a charging prediction model, including collecting key parameters such as the charging power demand of the electric vehicles currently connected to the charging pile, the remaining battery power, the start time and duration of charging; determine whether each electric vehicle is in a charging state at a certain moment, as well as its remaining mileage demand, and determine its contribution to the prediction model; finally, perform a weighted accumulation of the charging power demand of the electric vehicles currently connected to the charging pile at the prediction moment to obtain the estimated total charging power of the charging station at that moment.

[0082] Furthermore, the calculation formula of the charging prediction model is:

[0083]

[0084] Among them, P pred (t) is the estimated total charging power of the charging station at time t; P i (t) is the charging power demand of the electric vehicle currently connected to the charging pile i at time t, where i = 1, 2, 3...N, N = the number of charging piles; f(t,T i ,τ i ,D i ) is the indicator function, T i is the time when the electric vehicle starts charging when the charging pile i is currently connected, τ i is the charging duration of the electric vehicle currently connected to charging pile i, D i is the battery capacity of the electric vehicle currently connected to charging pile i at time t, with a value range of [0,1];

[0085] S121: Exponential function f(t,T i ,τ i ,D i ) are as follows:

[0086] If the remaining mileage demand is predicted to be D i =0, indicating that the battery of the electric vehicle currently connected to the charging pile i is fully charged at time t. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0;

[0087] If the remaining mileage demand is predicted to be D i is greater than 0 and less than the initial remaining mileage requirement, indicating that the battery of the electric vehicle currently connected to the charging pile i is not fully charged at time t, and T i ≤t≤T i +τ i , which means that at time t, the charging pile i is currently connected to the electric vehicle and is in the charging state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 1;

[0088] If the remaining mileage demand is predicted to be D i is equal to the initial remaining mileage requirement, and T i >t, indicating that at time t, the electric vehicle currently connected to the charging pile i is in an uncharged state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0;

[0089] Furthermore, in order to adapt to the power load limit of transformers in old residential areas, an exponential adjustment function is further constructed to calculate the charging power demand P of electric vehicles. i (t) is dynamically constrained, expressed as:

[0090]

[0091] Where, is the adjusted target charging power; is the regulation coefficient, reflecting the power reduction strength when the load increases; L(t) is the grid transformer load at the current moment; L max is the maximum rated load of the grid transformer, exp() is the exponential function;

[0092] It should be noted that if the battery of an electric vehicle in the charging station is not full, and the exponential function f(t,T i ,τ i ,D i )=1, then the adjusted target charging power The charging power demand of the i-th electric vehicle at time t is used to calculate the charging prediction model. This step dynamically responds to the carrying capacity of the old power grid based on an exponential function, achieving flexible control of the grid load, with peak reduction and valley filling functions, and improving the overall intelligent scheduling level.

[0093] Furthermore, the above exponential function firstly takes into account the risk of overload of the grid transformer under high load, so it is necessary to actively reduce the load before the power approaches the rated upper limit; secondly, in order to ensure that the adjustment process is both smooth and has a certain fast response characteristic, an exponential function with the characteristic of "the higher the load, the more obvious the power reduction" is selected; by setting the adjustment coefficient By controlling the steepness of the reduction curve, the original power distribution is hardly affected at low loads, and the power is rapidly attenuated to a safe range at high loads. This exponential weight adjustment method takes into account both response speed and system stability, preventing transformer damage due to excessive instantaneous loads and improving the overall system's intelligent adjustment capabilities during peak periods.

[0094] S2: Collect temperature data of the charging pile cable and determine whether there is an overheating risk. If there is an overheating risk, suspend charging; otherwise, enter S3.

[0095] Specifically, cable temperature data is obtained through a temperature sensor;

[0096] Exemplarily, determining the overheating risk includes:

[0097] If the cable temperature is greater than or equal to the temperature threshold, it is determined that there is an overheating risk and charging should be suspended;

[0098] On the other hand, if the cable temperature is less than the temperature threshold, it is determined that there is no overheating risk, and the following steps are continued.

[0099] S3: Predict the total charging power of the charging station at a certain moment based on the charging prediction model, and calculate the grid transformer load rate based on the total charging power.

[0100] S31: Calculate the transformer load rate of the power grid where the charging station is located.

[0101] The calculation formula for the transformer load rate of the power grid where the charging station is located is:

[0102]

[0103] Among them, S t is the transformer load factor at time t, P max is the rated maximum load of the transformer.

[0104] S4: The specific situation of charging decision based on user priority level, cable temperature data and transformer load rate is as follows:

[0105] If there is a risk of overheating, charging needs to be stopped;

[0106] Pre-set a first limit value and a second limit value, so that the first limit value is less than the second limit value;

[0107] If there is no risk of overheating and the transformer load factor S t If the value is less than the first limit, it indicates a low-load period, allowing all priority users to charge;

[0108] If there is no risk of overheating and the transformer load factor S t If the value is greater than or equal to the first limit and less than or equal to the second limit, it indicates a medium load period, allowing high-priority and medium-priority users to charge;

[0109] If there is no risk of overheating and the transformer load factor S t If the load is greater than the second limit value, it indicates a high-load period, allowing high-priority charging;

[0110] The charging cable temperature threshold is usually set at 75°C to avoid overheating risks and ensure the safe and stable operation of the power grid.

[0111] The beneficial effect of the present invention is that, when pre-processing user charging needs, not only the remaining battery power is taken into account, but also the interaction factors of mileage demand, historical charging behavior and waiting time are introduced. The real-time load is adaptively adjusted through the Logistic function, and the scheduling strategy of peak and valley periods is effectively balanced to ensure that the charging needs of different users are reasonably met, thereby improving scheduling fairness and user experience. Secondly, based on the constructed charging prediction model, the present invention further introduces an exponential adjustment function to dynamically constrain the target charging power. When the grid load approaches the rated value, the power is automatically reduced to participate in the total power aggregation, realizing peak reduction and valley filling. It can be intelligently adjusted without manual intervention, significantly reducing the risk of grid load fluctuations.

[0112] Example 2

[0113] Embodiment 2 is the second embodiment of the present invention. This embodiment differs from the first embodiment in that it further provides an orderly charging scheduling system for electric vehicles in old communities, which is used to implement the orderly charging scheduling method for electric vehicles in old communities proposed in Embodiment 1. The system includes:

[0114] The data acquisition module is used to obtain the charging demand data of electric vehicles in the charging station, collect cable temperature data, and calculate and divide the charging priority according to the charging demand data;

[0115] A model building module is used to build a charging prediction model based on charging demand data;

[0116] A prediction module is used to calculate the estimated total charging power of the charging station based on the charging prediction model, and obtain the transformer load rate of the power grid where the charging station is located based on the estimated total charging power of the charging station;

[0117] The decision-making module is used to determine whether there is an overheating risk based on cable temperature data, and to make charging decisions based on user priority and the load rate of the charging station grid transformer.

[0118] The present disclosure may be a system, method and / or computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0119] A computer-readable storage medium can be a tangible device that can hold and store instructions for use by an instruction execution device. A computer-readable storage medium can be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination thereof. As used herein, a computer-readable storage medium is not to be construed as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through an electrical wire.

[0120] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions to be stored in the computer-readable storage medium in each computing / processing device.

[0121] The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., utilizing an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions. The electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0122] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for orderly charging and scheduling of electric vehicles in old communities, characterized by: The steps include: Obtain charging demand data for electric vehicles in charging stations, calculate and prioritize charging based on the charging demand data, and build a charging prediction model; Collect cable temperature data and determine whether there is an overheating risk. If there is an overheating risk, charging will be suspended. If there is no overheating risk, the estimated total charging power of the charging station is calculated based on the charging prediction model, and the transformer load factor of the power grid where the charging station is located is obtained based on the estimated total charging power of the charging station. Make charging decisions based on user priority and transformer load rate.

2. The method for orderly charging and scheduling of electric vehicles in old communities according to claim 1 is characterized in that: The charging requirement data includes charging power requirement, charging start time, charging duration, and battery remaining capacity.

3. The method for orderly charging and scheduling electric vehicles in old communities according to claim 2 is characterized in that: The calculation of the charging priority specifically includes: Calculate the proportion of power to be charged, mileage and charging times based on the remaining battery power, predicted remaining mileage demand, maximum mileage, historical charging times and maximum cumulative charging times of the electric vehicles connected to each charging station in the charging station; Construct an interaction factor function that reflects the load condition, set the proportion of power to be charged, mileage and charging times, and the weight coefficient of the interaction factor to calculate the comprehensive evaluation results of each electric vehicle; Dynamic weight parameters are set to process the comprehensive evaluation results and obtain the charging priority score of the electric vehicle.

4. The method for orderly charging and scheduling of electric vehicles in old communities according to claim 3 is characterized in that: The charging priority is divided as follows: Setting a first priority threshold and a second priority threshold for charging priority; If the calculated charging priority is less than the first priority threshold, it means that the charging priority of the electric vehicle is low priority; If the calculated charging priority is greater than or equal to the first priority threshold and less than the second priority threshold, it means that the charging priority of the electric vehicle is medium priority; If the calculated charging priority is greater than the second priority threshold, it means that the charging priority of the electric vehicle is high.

5. The method for orderly charging and scheduling of electric vehicles in old communities according to claim 1 is characterized in that: The construction of the charging prediction model includes: Collect the predicted remaining mileage demand, charging start time, and charging duration of the electric vehicle currently connected to the charging pile, and obtain the exponential function value of the electric vehicle currently connected to the charging pile based on the collected data; The charging power requirements of the electric vehicles currently connected to the charging piles are collected, and the charging power requirements of the electric vehicles currently connected to the charging piles in the charging station at the predicted time are weighted and accumulated with the exponential function value of the electric vehicle to obtain the estimated total charging power of the charging station at that moment.

6. The method for orderly charging and scheduling electric vehicles in old communities according to claim 5 is characterized in that: The exponential function f(t,T i ,τ i ,D i The specific situation is as follows: If the remaining mileage demand is predicted to be D i =0, indicating that the battery of the electric vehicle currently connected to the charging pile i is fully charged at time t. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0; If the remaining mileage demand is predicted to be D i is greater than 0 and less than the initial remaining mileage requirement, indicating that the battery of the electric vehicle currently connected to the charging pile i is not fully charged at time t, and t i ≤t≤T i +τ i , which means that at time t, the charging pile i is currently connected to the electric vehicle and is in the charging state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 1; If the remaining mileage demand is predicted to be D i is equal to the initial remaining mileage requirement, and T i >t, indicating that at time t, the electric vehicle currently connected to the charging pile i is in an uncharged state. At this time, the exponential function f(t,T i ,τ i ,D i ) is 0; The initial remaining mileage demand is the predicted remaining mileage demand when the electric vehicle starts charging.

7. The method for orderly charging and scheduling electric vehicles in old communities according to claim 5 is characterized in that: If the battery of an electric vehicle in the charging station is not full and the exponential function is 1, then an exponential adjustment function is constructed to dynamically adjust the target charging power, and the adjusted target charging power P i adj (t) is used as the charging power demand of the electric vehicle currently connected to the charging pile i at time t for the calculation of the charging prediction model; The exponential adjustment function includes: Collect the current load value of the transformer in real time, obtain the maximum rated load of the transformer, and calculate the current load ratio; Set an adjustment coefficient to reflect the extent of power reduction when the load increases. Combine the current load ratio and the adjustment coefficient to calculate the extent to which charging power needs to be reduced when the load increases using an exponential function. The current target charging power is combined with the extent to which the charging power needs to be reduced when the load increases to obtain the adjusted target charging power.

8. The method for orderly charging and scheduling electric vehicles in old communities according to claim 1 is characterized in that: The specific circumstances of charging decision making based on user priority and charging station grid transformer load rate are as follows: Setting a first limit value and a second limit value, so that the first limit value is less than the second limit value; If the transformer load rate of the power grid where the charging station is located is less than the first limit value, it indicates a low-load period, and all priority users are allowed to charge; If the transformer load rate of the power grid where the charging station is located is greater than or equal to the first limit value and less than or equal to the second limit value, it indicates a medium load period, and high-priority and medium-priority users are allowed to charge; If the transformer load rate of the power grid where the charging station is located is greater than the second limit value, it indicates a high-load period, and high-priority charging is allowed.

9. An orderly charging scheduling system for electric vehicles in old communities, used to implement the orderly charging scheduling method for electric vehicles in old communities as described in any one of claims 1 to 8, characterized in that: include: The data acquisition module is used to obtain the charging demand data of electric vehicles in the charging station, collect cable temperature data, and calculate and divide the charging priority according to the charging demand data; A model building module is used to build a charging prediction model based on charging demand data; A prediction module is used to calculate the estimated total charging power of the charging station based on the charging prediction model, and obtain the transformer load rate of the power grid where the charging station is located based on the estimated total charging power of the charging station; The decision-making module is used to determine whether there is an overheating risk based on cable temperature data, and to make charging decisions based on user priority and the load rate of the charging station grid transformer.

10. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 8.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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