A charging station energy management system and method based on charging reservation information

Abstract

This invention discloses a charging station energy management system based on charging reservation information, including a vehicle reservation information receiving module and a charging station monitoring module; a central processing unit (CPU) is equipped with a charging demand analysis module, a charging prediction analysis module, and an energy management control module, all connected via communication; the CPU is also connected to the charging piles at the charging station. Its advantages are: by combining vehicle reservation information with future demand predictions for the charging station, charging piles can be planned in advance, effectively improving the operating efficiency and user satisfaction of the charging station, and enhancing its stable operation; the energy management control module, based on electricity demand, comprehensively considers the current operating status of the charging station and the power grid, helping to reduce charging station operating costs and alleviate pressure on the power grid. Overall, this invention has good economic and social benefits.

Description

Technical fields:

[0001] This invention relates to the field of intelligent traffic management, and in particular to a charging station energy management system and method based on charging reservation information. Background technology:

[0002] With the increasing prevalence of electric vehicles, the demand for charging at highway service areas has surged, making efficient management of energy allocation at charging stations a critical issue. Traditional charging station management methods often rely on fixed charging pile allocation strategies. These methods are often ineffective in handling the large charging demands during peak hours, leading to low charging efficiency and excessively long waiting times for users. Furthermore, these methods typically lack prediction of electric vehicle arrival times and charging demand, resulting in a lack of flexibility and predictability in charging resource allocation.

[0003] Existing charging station management technologies primarily focus on improving the efficiency of individual charging piles, while paying less attention to the energy management and optimized scheduling of the entire charging station. These technologies often fall short when dealing with large-scale, dynamically changing charging demands. Therefore, there is an urgent need for a technological solution that enables intelligent and efficient management of charging stations in highway service areas to address the shortcomings of existing technologies. Summary of the Invention:

[0004] One object of the present invention is to provide a charging station energy management system based on charging reservation information.

[0005] The technical solution is as follows: a charging station energy management system based on charging reservation information, including a central processing unit, which is communicatively connected to the charging piles of the charging station; it also includes a vehicle reservation information receiving module and a charging station monitoring module;

[0006] The central processing unit is equipped with a charging demand analysis module, a charging prediction analysis module, and an energy management control module that are connected in communication.

[0007] The vehicle reservation information receiving module is communicatively connected to the charging demand analysis module; the charging prediction analysis module is communicatively connected to the charging station monitoring module.

[0008] The charging demand analysis module, the charging station monitoring module, the charging prediction analysis module, and the charging pile are all communicatively connected to the energy management and control module.

[0009] The vehicle reservation information receiving module receives vehicle reservation information and preprocesses the received reservation information; the charging station monitoring module receives charging station operation information and preprocesses the received operation information.

[0010] The charging prediction and analysis module obtains historical operating data of the charging station through the charging station monitoring module, and predicts the future charging demand of the charging station based on the obtained historical operating data; the charging demand analysis module analyzes the current charging demand of the vehicle based on the information from the vehicle reservation information receiving module.

[0011] The energy management control module receives charging demand information output by the charging demand analysis module, charging station monitoring information output by the charging station monitoring module, and prediction information output by the charging prediction analysis module, and dynamically adjusts the allocation of charging piles based on the received information.

[0012] Preferably, it also includes a power grid interface module, and the central processing unit has a power grid analysis module;

[0013] The power grid analysis module is communicatively connected to the power grid interface module and the energy management control module, respectively; the power grid analysis module obtains power grid information through the power grid interface module and analyzes and predicts the power grid operating status.

[0014] The energy management control module dynamically adjusts the allocation of charging piles based on the grid operation status output by the grid analysis module.

[0015] Preferably, it also includes a user operation terminal, which is communicatively connected to the vehicle reservation information receiving module and the energy management control module.

[0016] Preferably, the user operation terminal and the vehicle reservation information receiving module are connected via a V2X network.

[0017] Another objective of this invention is to provide a charging station energy management method based on charging reservation information.

[0018] The following technical solutions are implemented: The energy management method for charging stations includes the following steps:

[0019] Step 1: The vehicle reservation information receiving module receives the vehicle reservation information and transmits the preprocessed information to the charging demand analysis module;

[0020] The charging station monitoring module monitors the real-time operating status of the charging station, and the charging prediction and analysis module obtains the historical operating data of the charging station through the charging station monitoring module.

[0021] Step 2: The charging demand analysis module analyzes the charging demand based on the received information and transmits the results to the energy management control module;

[0022] The charging prediction and analysis module predicts the future charging demand of the charging station based on the received historical operating data.

[0023] Step 3: The energy management control module dynamically adjusts the allocation status of the charging piles based on the information transmitted by the charging station monitoring module, the charging demand analysis module, and the charging prediction analysis module.

[0024] Preferably, in step two, the charging demand analysis module performs the following steps:

[0025] S2011: Collect historical charging demand information of vehicles and establish a charging demand analysis model;

[0026] S2012: Use the information collected in step S2011 to train the charging demand analysis model to obtain a trained charging demand analysis model.

[0027] S2013: Use the charging demand analysis model trained in step S2012 to analyze and predict the current charging demand of the vehicle, and transmit the prediction results to the energy management control module.

[0028] The charging demand analysis is a time series analysis model.

[0029] Preferably, in step two, the charging prediction and analysis module performs the following operations:

[0030] S2021: Collect historical operation data of charging stations and establish a charging prediction and analysis model;

[0031] S2022: Use the information collected in step S2021 to train the charging prediction analysis model to obtain a trained charging prediction analysis model;

[0032] S2023: Use the charging prediction analysis model trained in step S2022 to predict the overall future charging demand of the charging station, and transmit the prediction results to the energy management control module;

[0033] The charging prediction analysis model includes a clustering algorithm model and a time series analysis model.

[0034] Preferably, step one further includes: the power grid interface module receiving power grid operation information and transmitting the received information to the power grid analysis module;

[0035] Step two also includes: the power grid analysis module predicts and analyzes the power grid operation status based on the received operation information, and transmits the results to the energy management and control module.

[0036] Preferably, in step three, the energy management control process of the energy management control module includes:

[0037] S3011: The energy management control module receives real-time monitoring data from the charging station monitoring module, analysis data from the charging demand analysis module, and prediction data from the charging prediction analysis module;

[0038] S3012: The energy management control module dynamically allocates and adjusts the charging piles based on the received data.

[0039] The advantages of this invention are as follows: By combining vehicle reservation information with future demand forecasts for charging stations, charging piles can be planned in advance, which effectively improves the operating efficiency and user satisfaction of charging stations and enhances their stable operation. The energy management and control module, based on electricity demand, comprehensively considers the current operating status of the charging station and the power grid, which helps to reduce the operating costs of charging stations and alleviate the pressure on the power grid. Overall, this invention has good economic and social benefits. Attached image description:

[0040] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0041] Figure 1 This is a system connection diagram for Embodiment 1 of the present invention.

[0042] Figure 2 This is a flowchart of the energy management method for charging stations in Example 2.

[0043] Figure 3 This is the execution flowchart of the charging demand analysis module in Example 2.

[0044] Figure 4 This is the execution flowchart of the charging prediction analysis module in Example 2. Detailed implementation method:

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] Example 1:

[0047] like Figure 1 The diagram illustrates a charging station energy management system based on charging reservation information, comprising a central processing unit (CPU), a vehicle reservation information receiving module, and a charging station monitoring module. The CPU is communicatively connected to the charging piles at the charging station, controlling the charging piles. The vehicle reservation information receiving module receives vehicle charging reservation information, and the charging station monitoring module monitors the operating status of the charging station.

[0048] Specifically, the central processing unit is equipped with a charging demand analysis module, a charging prediction analysis module, and an energy management control module that are connected via communication.

[0049] Furthermore, the vehicle reservation information receiving module is communicatively connected to the charging demand analysis module. The vehicle reservation information receiving module receives vehicle reservation information and preprocesses the received reservation information. The charging demand analysis module receives information from the vehicle reservation information receiving module and analyzes the current charging demand of the vehicle. In this embodiment, the vehicle communicates with the vehicle reservation information receiving module through V2X (Vehicle-to-Everything) technology. Through V2X communication, the charging station can understand the estimated arrival time, charging demand (including estimated charging amount, charging speed, etc.) and historical charging data of each electric vehicle, thereby better planning the use and scheduling of charging piles.

[0050] The charging prediction and analysis module is connected to the charging station monitoring module. The charging station monitoring module receives the operating information of the charging station and preprocesses the received operating information. The charging prediction and analysis module obtains the historical operating data of the charging station through the charging station monitoring module and predicts the future charging demand of the charging station based on the obtained historical operating data.

[0051] By using the above modules as real-time data input and combining it with charging prediction analysis based on historical data, accurate prediction and optimization of future demand can be achieved.

[0052] Furthermore, the charging demand analysis module, charging station monitoring module, charging prediction analysis module, and charging pile are all communicatively connected to the energy management control module. The energy management control module receives charging demand information output by the charging demand analysis module, charging station monitoring information output by the charging station monitoring module, and prediction information output by the charging prediction analysis module, and dynamically adjusts the allocation of charging piles based on the received information.

[0053] To reduce the impact of charging stations on the power grid, the charging station energy management system also includes a power grid interface module, and the central processing unit has a power grid analysis module that communicates with the power grid interface module and the energy management control module.

[0054] The power grid analysis module obtains power grid information through the power grid interface module and analyzes and predicts the power grid operating status; the energy management and control module dynamically adjusts the allocation of charging piles based on the power grid operating status output by the power grid analysis module.

[0055] To facilitate user operation, the system is also equipped with a user operation terminal. The user operation terminal is connected to the vehicle reservation information receiving module and the energy management control module via V2X network communication. The user operation terminal sends reservation-related information to the vehicle reservation information receiving module and displays the charging strategy formulated by the energy management control system.

[0056] Example 2:

[0057] This embodiment provides a charging station energy management method, implemented based on the charging station energy management system of Embodiment 1. Specifically, as follows... Figure 2 As shown, this management method includes the following steps:

[0058] Step 1: The vehicle reservation information receiving module receives the vehicle reservation information and transmits the pre-processed information to the charging demand analysis module.

[0059] The charging station monitoring module monitors the real-time operating status of the charging station, while the charging prediction and analysis module obtains the historical operating data of the charging station through the charging station monitoring module.

[0060] The power grid interface module receives power grid operation information and transmits the received information to the power grid analysis module.

[0061] Step 2: The charging demand analysis module analyzes the charging demand based on the received information and transmits the results to the energy management control module;

[0062] The charging prediction and analysis module predicts the future charging demand of the charging station based on the received historical operating data;

[0063] The power grid analysis module predicts and analyzes the power grid operation status based on the received operation information, and transmits the results to the energy management and control module.

[0064] Step 3: The energy management and control module dynamically adjusts the allocation of charging piles based on the information transmitted by the charging station monitoring module, charging demand analysis module, charging prediction analysis module, and power grid analysis module.

[0065] To implement this energy management method, vehicles and charging stations communicate via a V2X communication network. The existing network layout and components can be used for the V2X communication network, which will not be elaborated upon here.

[0066] Furthermore, such as Figure 3As shown, in step two, the charging demand analysis module performs the following steps:

[0067] S2011: Collect historical charging demand information of vehicles and establish a charging demand analysis model;

[0068] S2012: Use the information collected in step S2011 to train the charging demand analysis model and obtain the trained charging demand analysis model.

[0069] S2013: Use the charging demand analysis model trained in step S2012 to analyze and predict the current charging demand of the vehicle, and transmit the prediction results to the energy management control module.

[0070] In this embodiment, the charging demand analysis model is a time series ARIMA model (autoregressive integral moving average model). Based on the predetermined information sent by the vehicle, the ARIMA model is used to predict the time when the vehicle arrives at the charging station and the demand during charging.

[0071] like Figure 4 As shown, in step two, the charging prediction analysis module performs the following operations:

[0072] S2021: Collect historical operation data of charging stations and establish a charging prediction and analysis model;

[0073] S2022: Use the information collected in step S2021 to train the charging prediction analysis model and obtain the trained charging prediction analysis model.

[0074] S2023: Use the charging prediction analysis model trained in step S2022 to predict the overall future charging demand of the charging station, and transmit the prediction results to the energy management control module.

[0075] The charging prediction and analysis model includes a clustering algorithm model and a time series analysis model. The time series analysis model can obtain the periodicity and trend changes of the overall charging demand of charging stations, thus predicting the charging demand for a future period. The clustering algorithm model groups charging events at charging stations to identify the charging habits of different user groups, such as regular commuters and long-distance drivers, thereby enabling the development of different charging strategies for these user groups.

[0076] Specifically, in this embodiment, when using a time series analysis model to predict the overall demand for charging stations, the prediction model is based on acquired historical charging data of charging stations, time (such as time periods of day, days of the week), and special events (such as holidays), so that the prediction results of the time series analysis model can meet the demand fluctuations at specific times (such as holidays, weekdays, seasonality). The prediction process is as follows:

[0077] ① Data Collection: Collect historical charging data, including the time, duration, and amount of charge for each vehicle charge. Also record time-related factors, such as time of day, weekdays, and special events (such as holidays or special activities).

[0078] ② Feature Engineering: Transform time data into usable features. Encode time into hours of the day and days of the week, and mark special events as binary variables (e.g., 1 for holidays and 0 for non-holidays).

[0079] ③ Establish a predictive model: Use linear regression to establish the following charging demand model:

[0080] β0 + β1 × hour + β2 × working day + β3 × holiday + ... + ε;

[0081] Where β0, β1, β2, ... are model parameters, and ε is the error term.

[0082] ④ Model training and validation: Train the model using historical charging data and evaluate the model's accuracy and generalization ability through validation sets or cross-validation.

[0083] ⑤ Implementation of Forecasting: Utilize the trained model to predict upcoming charging demand. Considering current time characteristics and upcoming special events, the model will output a predicted charging demand for a future period.

[0084] Specifically, in step three, the energy management control process of the energy management control module includes the following steps:

[0085] S3011: The energy management control module receives real-time monitoring data from the charging station monitoring module, analysis data from the charging demand analysis module, grid information output by the grid analysis module, and prediction data from the charging prediction analysis module.

[0086] S3012: The energy management control module dynamically allocates and adjusts the charging piles based on the received data.

[0087] Specifically, the control process of the energy management control system is as follows:

[0088] ① Define optimization objectives: Select energy management and control objectives, such as optimizing the match between charging demand and grid capacity, maximizing overall charging efficiency, or minimizing total charging time. Charging stations can determine these objectives based on actual usage needs.

[0089] ② Define the objective function: Define the objective function based on the selected optimization objective. For example, if the objective is to minimize the total charging time, the objective function would be:

[0090]

[0091] Where N is the number of vehicles, M is the number of charging stations, and t ij It is the charging time of vehicle i at charging station j, x ij It is a binary decision variable indicating whether vehicle i is charging at charging station j.

[0092] ③ Set constraints: Constraints include restrictions on the allocation of charging piles, grid load restrictions, and vehicle charging demand.

[0093] Each vehicle can only be assigned to one charging station:

[0094] Each charging station can only serve one vehicle at a time.

[0095] The grid load does not exceed the maximum capacity:

[0096] Among them, P ij It is the charging power, P max It is the maximum load on the power grid.

[0097] ④ Make predictions: Use a linear programming solver to solve the above equations to find the optimal charging pile allocation scheme. Then apply the obtained optimal allocation scheme to the actual charging station scheduling to ensure that each vehicle receives charging at the best time and location, while keeping the grid load within an acceptable range.

[0098] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A charging station energy management system based on charging reservation information, comprising a central processor in communication with charging piles of a charging station; characterized in that, Also include vehicle reservation information receiving module, charging station monitoring module; The central processing unit is provided with a communication connected charging demand analysis module, a charging prediction analysis module and an energy management control module; The vehicle reservation information receiving module is in communication connection with the charging demand analysis module; the charging prediction analysis module is in communication connection with the charging station monitoring module; The charging demand analysis module, the charging station monitoring module, the charging prediction analysis module and the charging pile are in communication connection with the energy management control module; The vehicle reservation information receiving module receives the reservation information of the vehicle and pre-processes the received reservation information; the charging station monitoring module receives the operation information of the charging station and pre-processes the received operation information; The charging prediction analysis module obtains the historical operation data of the charging station through the charging station monitoring module, and predicts the future charging demand of the charging station based on the obtained historical operation data; the charging demand analysis module analyzes the charging demand of the current vehicle according to the information of the vehicle reservation information receiving module; The energy management control module receives the charging demand information output by the charging demand analysis module, the charging station monitoring information output by the charging station monitoring module and the prediction information output by the charging prediction analysis module respectively, and dynamically adjusts the distribution of the charging pile according to the received information; the control process of the energy management control module is as follows: ① Define the optimization target: select the target of energy management control; ② Set the objective function: set the objective function according to the selected optimization target, and set the objective function as follows: , where N is the number of vehicles, M is the number of charging piles, is the charging time of vehicle i at charging pile j, is a binary decision variable indicating whether vehicle i is charging at charging pile j; ③ Set the constraint condition: the constraint condition includes the distribution limit of the charging pile, the load limit of the power grid and the charging demand of the vehicle; Each vehicle can only be assigned to one charging column: ; Each charging pile can only serve one vehicle at a time: ; The grid load does not exceed the maximum capacity: , wherein, is the charging power, is the maximum load of the power grid; ④ Make a prediction: use a linear programming solver to solve the above equation to find the optimal charging pile distribution scheme; and apply the optimal distribution scheme obtained by solving to the actual charging station scheduling to ensure that each vehicle receives charging at the best time and location while keeping the power grid load within an acceptable range; Also include a power grid interface module, and the central processing unit is provided with a power grid analysis module in communication connection with the power grid interface module and the energy management control module; The power grid analysis module obtains power grid information through the power grid interface module and analyzes and predicts the operation state of the power grid; The energy management control module dynamically adjusts the distribution of the charging pile in combination with the power grid operation state output by the power grid analysis module; The charging demand analysis module performs the following steps: S2011: Collect historical charging demand information of the vehicle and establish a charging demand analysis model; S2012: Use the information collected in step S2011 to train the charging demand analysis model to obtain a trained charging demand analysis model; S2013: Use the trained charging demand analysis model in step S2012 to analyze and predict the charging demand of the current vehicle, and transmit the prediction result to the energy management control module; The charging demand analysis model is a time series analysis model; The charging prediction analysis module performs the following operations: S2021: Collect historical operation data of the charging station, and establish a charging prediction analysis model; S2022: Train the charging prediction analysis model using the information collected in step S2021 to obtain a trained charging prediction analysis model; S2023: Use the trained charging prediction analysis model in step S2022 to predict the future overall charging demand of the charging station, and transmit the prediction result to the energy management control module; The charging prediction analysis model includes a clustering algorithm model and a time series analysis model, and the time series analysis model is: ； wherein, are model parameters, is an error term.

2. The charging station energy management system based on charging reservation information according to claim 1, characterized in that, Further comprising a user operation terminal, which is in communication connection with the vehicle reservation information receiving module and the energy management control module.

3. The charging station energy management system based on charging reservation information according to claim 2, characterized in that, The user operation terminal and the vehicle reservation information receiving module are in communication connection through a V2X network.

4. A charging station energy management method based on charging reservation information, characterized by, The application of the charging station energy management system based on charging reservation information according to claim 1 comprises the following steps: Step one: The vehicle reservation information receiving module receives the reservation information of the vehicle, and transmits the preprocessed information to the charging demand analysis module; The charging station monitoring module monitors the real-time operation state of the charging station, and the charging prediction analysis module obtains the historical operation data of the charging station through the charging station monitoring module; Step two: The charging demand analysis module analyzes the charging demand according to the received information, and transmits the result to the energy management control module; The charging prediction analysis module predicts the future charging demand of the charging station according to the received historical operation data; Step three: The energy management control module dynamically adjusts the allocation status of the charging piles according to the information transmitted by the charging station monitoring module, the charging demand analysis module and the charging prediction analysis module.

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