New energy automobile charging station battery intelligent charging and discharging optimization method and system

Abstract

The invention relates to the technical field of new energy automobile charging management, in particular to an intelligent charging and discharging optimization method and system for a new energy automobile charging station battery, and the method comprises the steps: constructing a charging demand prediction model through employing a long short-term neural network LSTM model, and predicting the charging demand of a future time period; constructing a multi-type optimized charging and discharging strategy which comprises a peak load shifting strategy and a new energy consumption strategy; a battery charging efficiency evaluation model is constructed, the charging efficiency is monitored in real time, and abnormity is warned in time; based on the evaluated battery charging efficiency, constructing a battery charging scheduling model of the charging station, and when a plurality of batteries are charged at the same time, solving a global optimal charging power distribution scheme; according to the method, multiple aspects of charging demand prediction, charging and discharging strategy optimization, battery efficiency evaluation, charging scheduling and the like are comprehensively considered, and an effective solution is provided for intelligent operation of the new energy automobile charging station.

Description

Technical Field

[0001] The present invention relates to the technical field of new energy vehicle charging management, and specifically to a method and system for optimizing the intelligent charging and discharging of batteries in a new energy vehicle charging station. Background Art

[0002] With the popularization of new energy vehicles, the construction and operation of charging stations face many challenges. In order to improve the operational efficiency of charging stations and optimize resource allocation, it is necessary to accurately predict the charging demand in the future.

[0003] During operation, charging stations face the problems of grid load fluctuations and unstable renewable energy generation. In order to balance the grid load, charging stations need to reasonably arrange charging and discharging strategies, reduce charging during peak grid load periods, and increase charging during low load periods. At the same time, in order to promote the consumption of renewable energy, charging stations need to give priority to renewable energy generation. However, most of the existing charging and discharging strategies are based on fixed rules, lack flexibility and adaptability, and are difficult to cope with dynamically changing grid conditions and renewable energy generation. During the charging process, the charging efficiency of the battery is affected by parameters such as voltage, current, and temperature. When the charging station charges multiple batteries at the same time, how to reasonably allocate the charging power is also a key issue. The charging efficiency and health status of different batteries are different. Simply allocating the charging power evenly may lead to low overall charging efficiency and prolong the charging time. Therefore, it is necessary to dynamically adjust the charging power distribution according to the real-time status of the battery and the load of the charging station to achieve the global optimum.

[0004] The operation of new energy vehicle charging stations faces challenges in many aspects, such as charging demand prediction, charging and discharging strategy optimization, battery charging efficiency evaluation, and charging power scheduling. Traditional methods are difficult to effectively deal with these challenges, and it is urgent to develop intelligent optimization methods to improve the operating efficiency and service quality of charging stations.

[0005] In view of this, the present invention proposes a method and system for optimizing the intelligent charging and discharging of batteries in a new energy vehicle charging station. Summary of the invention

[0006] To achieve the above purpose, the present invention provides a method and system for optimizing the intelligent charging and discharging of batteries in a new energy vehicle charging station. The specific technical scheme is as follows: The method for optimizing the intelligent charging and discharging of batteries in a new energy vehicle charging station comprises:

[0007] Construct a charging demand prediction model based on the long-term and short-term neural network LSTM model, use historical charging data, and combine date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future time period;

[0008] Based on the charging demand of new energy vehicles in the future time period, build multi-type optimized charging and discharging strategies for new energy vehicle charging stations, and respectively build peak shaving and valley filling strategies and new energy consumption strategies;

[0009] Construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect the battery's voltage, current, and temperature data under constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found;

[0010] Based on the evaluated battery charging efficiency, a battery charging scheduling model for the charging station is constructed to solve the global optimal charging power allocation scheme when charging multiple batteries.

[0011] Preferably, historical charging data is obtained, including N charging records, wherein the charging records include information on real-time charging power consumption, peak charging power consumption, and charging power, and the charging records are integrated into a data set;

[0012] Collect date type and weather data related to charging demand and merge them with the charging record dataset to form a feature dataset;

[0013] Construct a long-term and short-term neural network LSTM model as a charging demand prediction model; define the output layer of the LSTM model, set the output dimension to the length p of the future time period, and select the mean square error MSE as the loss function;

[0014] The feature data set X and the corresponding charging demand value Y are divided into a training set and a test set according to a predetermined ratio, the LSTM model is trained using the training set data, and the model parameters are updated through the back propagation algorithm and optimizer;

[0015] Use the test set data to evaluate the trained LSTM model and calculate the prediction performance indicators of the model on the test set; adjust the model's hyperparameters based on the model's prediction performance, and repeat the training and evaluation process until the prediction performance indicators meet the requirements;

[0016] Based on the trained charging demand prediction model, charging demand prediction is performed for a given future time period T = t1, t2, ..., t p , construct feature vectors of date type and weather factors As the input of the charging demand prediction model; t p is the tth time period in the future p A point in time, For the tth p The feature vectors of date type and weather factors at each time point;

[0017] Using the trained charging demand prediction model, the feature vector XT Make predictions to obtain the charging demand forecast for the future time period in Indicates the tth time period in the future p The predicted value of charging demand at a time point;

[0018] The charging demand forecast value Output serves as input for subsequent optimization of charging and discharging strategies.

[0019] Preferably, construct multiple types of optimized charging and discharging strategies for new energy vehicle charging stations, including peak shaving and valley filling strategies and new energy consumption strategies;

[0020] The objective function of the peak-shaving and valley-filling strategy is defined to minimize the charging load of the charging station during peak hours and maximize the charging amount during valley hours. The objective function of the peak-shaving and valley-filling strategy is expressed as: Where t is the time in the future time period T, T peak represents the set of peak hours, T valley represents the set of low periods, P t represents the charging power at future time t;

[0021] The constraints of the peak-shaving and valley-filling strategy include: charging power constraint: 0≤P t ≤P max , And the charge capacity constraint:∑ t∈T P t Δt=E demand ; Among them, P max represents the maximum charging power of the charging station, T represents the future time period set, Δt represents the time interval, and E demand Represents the predicted total charging demand.

[0022] Preferably, the objective function of the new energy consumption strategy is defined, and the goal is to maximize the amount of renewable energy consumed by the charging station; the objective function of the new energy consumption strategy is expressed as: max∑ t∈T P t ·R t , where R t It represents the proportion of renewable energy power generation in the total power generation at the future time t;

[0023] The constraints of the new energy consumption strategy include charging power constraints: 0≤P t ≤P max , Charge capacity constraint: ∑ t∈T P t Δt=E demand , and renewable energy generation constraint: 0≤R t≤1,

[0024] Preferably, the objective functions of the peak shaving and valley filling strategy and the new energy consumption strategy are weighted and summed by the weighted summation method to construct a multi-objective optimization problem; the objective function after weighted summation is as follows:

[0025]

[0026] Among them, λ1 and λ2 are the weight coefficients of the peak shaving and valley filling strategy and the new energy consumption strategy respectively;

[0027] The constraints for solving the multi-objective optimization include the charging power constraint: 0≤P t ≤P max , Charge capacity constraint: ∑ t∈T P t Δt=E demand ; and renewable energy generation capacity constraint: 0≤R t ≤1,

[0028] Preferably, the battery is charged constantly for a period of time, and the parameters of voltage, current and temperature of the rechargeable battery are collected at fixed time intervals Δt during the constant charging process;

[0029] A charging efficiency scoring index S is defined to evaluate the charging efficiency of the battery; the value range of the charging efficiency scoring index S is [0,1], where S=1 indicates that the battery fully receives a constant charging output; S=0 indicates that the battery does not receive any charging output at all;

[0030] Calculate the cumulative charging output Q during constant charging out And the battery cumulative reception Q in : Among them I out,t is the constant charging output current at time t; Δt is the time interval; K is the total number of sampling points; Among them I in,t is the actual received current of the battery at time t;

[0031] Calculate the charging efficiency η, that is, the cumulative amount of battery received Q in and the accumulated charging output Q out Ratio of:

[0032]

[0033] Constructing the battery charging efficiency threshold η th , when η≥η th When η<η, it indicates that the battery charging efficiency is qualified; when η<η th, it indicates that the battery charging efficiency is unqualified, and a charging warning reminder is issued for the battery.

[0034] Preferably, the objective function of the battery charging scheduling model of the charging station is defined to maximize the weighted total charging efficiency score while satisfying the load constraints of the charging station: Where M is the total number of batteries in the charging station; w j is the weight coefficient of the jth battery; S j is the charging efficiency score index of the jth battery; P j is the charging power allocated to the jth battery;

[0035] The constraints of the battery charging scheduling model for charging stations include: Charging station load constraints: Where P station (t) is the maximum available charging power of the charging station at time t; Battery charging power constraint: 0≤P j ≤P j,max , Where P j,max is the maximum charging power of the jth battery; battery charging efficiency constraint: S j ≥S th , Where S th is the threshold value of the battery charging efficiency score indicator;

[0036] Solve the battery charging scheduling model of the charging station and transform the optimization problem into a linear programming problem:

[0037]

[0038] The linear programming algorithm is used to solve the linear programming problem and the optimal solution P is obtained. best =[P′1,P'2...,P' M ], where P′ M represents the optimal charging power allocated to the Mth battery;

[0039] When the charging station is not fully loaded, No charging power is allocated to the rechargeable battery;

[0040] When the charging station is fully loaded, According to the optimal charging power allocation scheme P best Allocate charging power to each battery. represents the optimal charging power assigned to the jth battery, j∈[1,M].

[0041] A new energy vehicle charging station battery intelligent charging and discharging optimization system, which is used to implement the new energy vehicle charging station battery intelligent charging and discharging optimization method, including: a charging demand prediction module, a multi-type optimization module, a charging efficiency evaluation module and a charging scheduling module;

[0042] The charging demand prediction module is used to build a charging demand prediction model based on the long-term and short-term neural network LSTM model, using historical charging data and combining date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future time period;

[0043] The multi-type optimization module constructs a multi-type optimization charging and discharging strategy for new energy vehicle charging stations based on the charging demand of new energy vehicles in the future time period, and constructs a peak shaving and valley filling strategy and a new energy consumption strategy respectively;

[0044] The charging efficiency evaluation module is used to construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect data on the voltage, current, and temperature of the battery under the constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found;

[0045] The charging scheduling module constructs a battery charging scheduling model for the charging station based on the evaluated battery charging efficiency, and solves a globally optimal charging power allocation scheme when charging multiple batteries.

[0046] An electronic device comprises: a processor and a memory, wherein the memory stores a computer program that can be called by the processor; the processor executes the intelligent charging and discharging optimization method for the battery of a new energy vehicle charging station by calling the computer program stored in the memory.

[0047] A computer-readable storage medium stores instructions, which, when executed on a computer, enable the computer to execute the intelligent charging and discharging optimization method for batteries in a new energy vehicle charging station.

[0048] Beneficial effects of the present invention: The present invention establishes an accurate charging demand prediction model by utilizing historical charging data and combining influencing factors such as date and weather; by predicting the charging demand of new energy vehicles in the future, it provides an important basis for the operation and resource allocation of charging stations.

[0049] Based on the charging demand prediction results, the present invention constructs a peak shaving and valley filling strategy and a new energy consumption strategy, which can effectively cope with the problems of grid load fluctuation and intermittent power generation of new energy; the peak shaving and valley filling strategy balances the grid load and improves the grid operation efficiency by increasing charging during the low load period of the grid and reducing charging during the peak period; the new energy consumption strategy gives priority to the use of renewable energy for power generation, reduces fossil energy consumption, promotes the efficient use of new energy, and achieves the goals of environmental protection and energy conservation.

[0050] The present invention evaluates the charging efficiency of the battery by constantly charging the battery and collecting data such as voltage, current, and temperature. It can promptly detect abnormal conditions during the battery charging process, such as a decrease in charging efficiency and abnormal temperature, so as to issue an early warning and ensure charging safety.

[0051] Based on the battery charging efficiency evaluation results, the present invention constructs a battery charging scheduling model for the charging station, which can solve the global optimal charging power allocation scheme when multiple batteries are charged at the same time; by reasonably allocating the charging power, the overall charging efficiency of the charging station can be maximized, providing users with better charging services. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of the intelligent charging and discharging optimization method for batteries in new energy vehicle charging stations provided by the present invention;

[0053] Figure 2 This is a structural diagram of the new energy vehicle charging station battery intelligent charging and discharging optimization system provided by the present invention. DETAILED DESCRIPTION

[0054] In order to better understand the present invention, a more detailed description will be made of various aspects of the present invention with reference to the accompanying drawings. It should be understood that these detailed descriptions are only descriptions of exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention in any way. Throughout the specification, the same reference numerals refer to the same elements. The expression "and / or" includes any and all combinations of one or more of the associated listed items.

[0055] In the accompanying drawings, the size, dimensions and shapes of the elements have been slightly adjusted for ease of illustration. The accompanying drawings are for illustration only and are not strictly drawn to scale. As used herein, the terms "substantially", "approximately" and similar terms are used as terms of approximation, not as terms of degree, and are intended to illustrate the inherent deviations in measurements or calculations that will be recognized by those of ordinary skill in the art. In addition, in the present invention, the order in which the steps are described does not necessarily represent the order in which these processes occur in actual operation, unless otherwise specified or can be derived from the context.

[0056] It should also be understood that expressions such as "comprises", "including", "having", "includes" and / or "comprising" are open rather than closed expressions in this specification, which indicate the presence of the stated features, elements and / or components, but do not exclude the presence of one or more other features, elements, components and / or combinations thereof. In addition, when expressions such as "at least one of..." appear after a list of listed features, they modify the entire list of features rather than just the individual elements in the list. In addition, when describing embodiments of the present invention, "may" is used to mean "one or more embodiments of the present invention". And, the term "exemplary" is intended to refer to an example or illustration.

[0057] Unless otherwise defined, all words (including engineering terms and scientific and technological terms) used in this article have the same meaning as those commonly understood by ordinary technicians in the field to which the present invention belongs. It should also be understood that unless there is a clear explanation in the present invention, the words defined in the commonly used dictionary should be interpreted as having the same meaning as their meaning in the context of the relevant technology, and should not be interpreted in an idealized or overly formal sense.

[0058] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0059] Example 1

[0060] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for optimizing intelligent charging and discharging of batteries in new energy vehicle charging stations.

[0061] S1: Construct a charging demand prediction model based on the long-term and short-term neural network (LSTM) model. Use historical charging data and combine date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future.

[0062] Acquire historical charging data, including N charging records, which include information on real-time charging power consumption, peak charging power consumption, and charging power, and integrate the charging records into a data set D = {d1, d2, d i ,...,d N}, where d i represents the i-th charging record, and N is the total number of charging records.

[0063] Collect date type and weather data related to charging demand and merge them with charging record dataset to form feature dataset X = {x1, x2, x i ,...,x N}, where x iRepresents the feature vector corresponding to the i-th charging record, and the dimension of the feature vector is N.

[0064] Normalize the feature data set and scale the value range of each feature to the interval [0, 1]. The formula is: where x ij represents the jth eigenvalue of the i-th charging record, x′ ij represents the normalized eigenvalue.

[0065] Construct a long-short term neural network LSTM model as a charging demand prediction model; define the input layer of the LSTM model, take the feature data set X as input, and the input length is the dimension N of the feature vector; define the hidden layer of the LSTM model, set the appropriate number of hidden units h and the number of layers l, for example, 2 layers of LSTM, with 100 hidden units in each layer.

[0066] The main calculation formula of LSTM is as follows: Forget gate: f t =σ(W f ·[h t-1 ,x t ]+b f ); Input gate: i t =σ(W i ·[h t-1 ,x t ]+b i ); Output gate: o t =σ(W o ·[h t-1 ,x t ]+b o ); Candidate memory cell status: Memory cell status: Hidden state: h t =o t *tanh(C t ).

[0067] Among them, f t represents the output of the forget gate time step, with a value range of [0,1], which controls whether the memory cell state information of the previous time step is forgotten; σ represents the Sigmoid activation function, which is used to compress the input into the range of [0, 1]; W f and b f Respectively represent the weight matrix and bias term of the forget gate, h t-1 represents the hidden state at the previous time step t-1, x t Represents the input vector of the current time step t; i t represents the input gate output at time step t; W i and b i Represent the weight matrix and bias term of the input gate respectively; ot represents the output gate output at time step t; W o and b o Represent the weight matrix and bias term of the output gate respectively; represents the candidate memory cell state at time step t, encoding the new information of the current time step; tanh represents the hyperbolic tangent activation function, compressing the input into the range of [1, 1]; W C and b C Represent the weight matrix and bias term of candidate memory cells respectively; C t represents the state of the memory cell at time step t; h t represents the hidden state at time step t.

[0068] Define the output layer of the LSTM model and set the output dimension to the length p of the future time period. For example, if the charging demand in the next 24 hours is predicted, the output dimension is 24. Define the loss function of the LSTM model and select the mean square error MSE as the loss function.

[0069] The feature data set X and the corresponding charging demand value Y are divided into training set X according to a predetermined ratio. train ,Y train and the test set X test ,Y test For example, 80% of the data is used as the training set and 20% of the data is used as the test set; using the training set data X train ,Y train Train the LSTM model, update the model parameters through the back propagation algorithm and optimizer (such as Adam), and iterate until the model converges or reaches the preset number of iterations; use the test set data X test ,Y test Evaluate the trained LSTM model and calculate the prediction performance indicators of the model on the test set, including the mean absolute error (MAE) and the root mean square error (RMSE); adjust the model's hyperparameters based on the model's prediction performance, and repeat the training and evaluation process until the prediction performance indicators meet the requirements.

[0070] Based on the trained charging demand prediction model, charging demand prediction is performed for a given future time period T = t1, t2, ..., t p , construct feature vectors of date type and weather factors As the input of the charging demand prediction model; t p is the tth time period in the future p A point in time, For the tth p The feature vector of date type and weather factors at each time point.

[0071] Using the trained charging demand prediction model, the feature vector X T Make predictions to obtain the charging demand forecast for the future time period in Indicates the future time period t p The predicted value of charging demand at a certain point in time.

[0072] The charging demand forecast value Output serves as input for subsequent optimization of charging and discharging strategies.

[0073] In step S1, a charging demand prediction model based on LSTM is constructed, which combines historical charging data, date and weather factors to accurately predict the charging demand of new energy vehicles in the future time period; the prediction results provide reliable input data for the subsequent optimization of charging and discharging strategies.

[0074] S2: Based on the charging demand of new energy vehicles in the future time period, construct multi-type optimized charging and discharging strategies for new energy vehicle charging stations, and construct peak shaving and valley filling strategies and new energy consumption strategies respectively to solve the problems of supply and demand imbalance and energy waste.

[0075] The objective function of the peak-shaving and valley-filling strategy is defined to minimize the charging load of the charging station during peak hours and maximize the charging amount during valley hours. The objective function of the peak-shaving and valley-filling strategy is expressed as: Where t is the time in the future time period T, T peak represents the set of peak hours, T valley represents the set of low periods, P t represents the charging power at future time t.

[0076] The constraints of the peak-shaving and valley-filling strategy include: charging power constraint: 0≤P t ≤P max , And the charge capacity constraint:∑ t∈T P t Δt=E demand ; Among them, P max represents the maximum charging power of the charging station, T represents the future time period set, Δt represents the time interval, and E demand Represents the predicted total charging demand.

[0077] A linear programming algorithm is used to solve the optimal charging power allocation for the peak shaving and valley filling strategy. The objective function and constraints are converted into a standard linear programming form and solved using the simplex algorithm. The optimal charging power allocation for each time period is obtained. To achieve the purpose of reducing peak loads and filling valley loads.

[0078] Define the objective function of the new energy consumption strategy, the goal is to maximize the charging station's consumption of renewable energy (such as photovoltaic and wind power) and reduce the phenomenon of abandoned light and wind. The objective function of the new energy consumption strategy can be expressed as: max∑ t∈T P t ·R t , where R t It represents the proportion of renewable energy power generation in the total power generation in time period t.

[0079] The constraints of the new energy consumption strategy include charging power constraints: 0≤P t ≤P max , Charge capacity constraint: Σ t∈T P t Δt=E demand , and renewable energy generation constraint: 0≤R t ≤1,

[0080] The nonlinear programming algorithm is used to solve the optimal charging power allocation of the new energy consumption strategy; the objective function and constraints are converted into a standard nonlinear programming form and solved using a sequential quadratic programming algorithm; the optimal charging power allocation for each time period is obtained To achieve the goal of new energy consumption.

[0081] The objective functions of the peak shaving and valley filling strategy and the new energy consumption strategy are weighted and summed by the weighted summation method to construct a multi-objective optimization problem; the objective function after weighted summation is as follows:

[0082]

[0083] Among them, λ1 and λ2 are the weight coefficients of the peak shaving and valley filling strategy and the new energy consumption strategy respectively; the weight coefficients λ1 and λ2 of the peak shaving and valley filling strategy and the new energy consumption strategy can be dynamically adjusted according to the actual operating conditions to adapt to the changes in charging demand and renewable energy generation.

[0084] The constraints for solving the multi-objective optimization include the charging power constraint: 0≤P t ≤P max , Charge capacity constraint: Σ t∈T P t Δt=E demand ; and renewable energy generation capacity constraint: 0≤R t ≤1,

[0085] The solution algorithm uses nonlinear programming algorithm to solve multi-objective optimization problems and obtain the optimal charging power allocation that takes into account both peak shaving and valley filling and new energy consumption; it uses algorithms such as sequential quadratic programming or interior point method to solve and obtain the optimal charging power allocation for each time period.

[0086] According to the optimal charging power distribution obtained Formulate corresponding charging strategies and preferential policies to guide electric vehicle users to charge at appropriate time periods.

[0087] Based on the predicted charging demand, step S2 constructs a peak-shaving and valley-filling strategy and a new energy consumption strategy, and solves the optimal charging power allocation plan through linear programming and nonlinear programming algorithms. The peak-shaving and valley-filling strategy effectively reduces the charging load during peak hours and improves the charging capacity utilization rate during valley hours. The new energy consumption strategy maximizes the charging station's consumption of renewable energy and reduces the phenomenon of abandoned solar and wind power.

[0088] S3: Construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect the battery's voltage, current, and temperature data under constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found.

[0089] A charging efficiency scoring index S is defined to evaluate the charging efficiency of the battery. The value range of the charging efficiency scoring index S is [0,1], where S=1 indicates that the battery fully receives a constant charging output; and S=0 indicates that the battery does not receive any charging output.

[0090] Calculate the cumulative charging output Q during constant charging out And the battery cumulative reception Q in : Among them I out,t is the constant charging output current at time t; Δt is the time interval; K is the total number of sampling points; Among them I in,t is the actual received current of the battery at time t.

[0091] Calculate the charging efficiency η, that is, the cumulative amount of battery received Q in and the accumulated charging output Q out Ratio of:

[0092]

[0093] Constructing the battery charging efficiency threshold η th , when η≥η th When η<η, it indicates that the battery charging efficiency is qualified; when η<η th , it indicates that the battery charging efficiency is unqualified, and a charging warning reminder is issued for the battery.

[0094] Step S3 constructs a battery charging efficiency evaluation model, collects the battery's voltage, current, and temperature data under constant charging data, and evaluates the battery's charging efficiency. The charging efficiency score quantifies the battery's ability to receive constant charging output and provides an important reference for the battery's health status. When the charging efficiency is lower than the threshold, the model issues a warning in a timely manner, effectively preventing abnormal battery charging.

[0095] S4: Based on the evaluated battery charging efficiency, a battery charging scheduling model for the charging station is constructed to solve the global optimal charging power allocation scheme when charging multiple batteries.

[0096] The objective function of the battery charging scheduling model of the charging station is defined to maximize the weighted total charging efficiency score while satisfying the load constraints of the charging station: Where M is the total number of batteries in the charging station; w j is the weight coefficient of the jth battery; S j is the charging efficiency score index of the jth battery; P j is the charging power allocated to the jth battery;

[0097] The constraints of the battery charging scheduling model for charging stations include: Charging station load constraints: Where P station (t) is the maximum available charging power of the charging station at time t; Battery charging power constraint: 0≤P j ≤P j,max , Where P j,max is the maximum charging power of the jth battery; battery charging efficiency constraint: S j ≥S th , Where S th is the threshold value of the battery charging efficiency score indicator;

[0098] Solve the battery charging scheduling model of the charging station: Convert the optimization problem into a linear programming problem:

[0099]

[0100] The linear programming algorithm is used to solve the linear programming problem and the optimal solution P is obtained. best =[P′1,P'2...,P' M ], where P' M represents the optimal charging power allocated to the Mth battery;

[0101] When the charging station is not fully loaded, No charging power is allocated to the rechargeable battery;

[0102] When the charging station is fully loaded, According to the optimal charging power allocation scheme P best The charging power is distributed to each battery, where represents the optimal charging power assigned to the jth battery, j∈[1,M].

[0103] In step S4, a battery charging scheduling model for the charging station is constructed based on the evaluated battery charging efficiency. The global optimal charging power allocation scheme is obtained by solving the linear programming algorithm while satisfying the load constraints of the charging station.

[0104] Example 2

[0105] Reference Figure 2 , which is the second embodiment of the present invention, provides a new energy vehicle charging station battery intelligent charging and discharging optimization system.

[0106] The system includes: a charging demand prediction module, a multi-type optimization module, a charging efficiency evaluation module and a charging scheduling module.

[0107] The charging demand prediction module is used to build a charging demand prediction model based on the long-term and short-term neural network LSTM model, and uses historical charging data, combined with date and weather factors, to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future time period.

[0108] The multi-type optimization module constructs a multi-type optimization charging and discharging strategy for new energy vehicle charging stations based on the charging demand of new energy vehicles in the future time period, and constructs a peak shaving and valley filling strategy and a new energy consumption strategy respectively.

[0109] The charging efficiency evaluation module is used to construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect data on the voltage, current, and temperature of the battery under the constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found.

[0110] The charging scheduling module constructs a battery charging scheduling model for the charging station based on the evaluated battery charging efficiency, and solves a globally optimal charging power allocation scheme when charging multiple batteries.

[0111] Example 3

[0112] The present invention also provides an electronic device. The electronic device may include one or more processors and one or more memories. The memories store computer-readable codes, and when the computer-readable codes are executed by the one or more processors, the intelligent charging and discharging optimization method for batteries in a new energy vehicle charging station as described above may be executed.

[0113] The method or system according to the embodiment of the present invention may also be implemented with the aid of the architecture of the electronic device of the present invention.

[0114] An electronic device may include a bus, one or more CPUs, read-only memory (ROM), random access memory (RAM), a communication port for connecting to a network, input / output components, a hard disk, and the like.

[0115] A storage device in an electronic device, such as a ROM or a hard disk, can store the intelligent charging and discharging optimization method for a battery in a new energy vehicle charging station provided by the present invention.

[0116] The intelligent charging and discharging optimization method of batteries in new energy vehicle charging stations includes: constructing a charging demand prediction model based on a long-term and short-term neural network (LSTM) model, using historical charging data, and combining date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in future time periods; based on the charging demand of new energy vehicles in future time periods, constructing multiple types of optimized charging and discharging strategies for new energy vehicle charging stations, and respectively constructing a peak shaving and valley filling strategy and a new energy consumption strategy; constructing a battery charging efficiency evaluation model, charging the battery using constant charging data over a period of time, collecting data on the voltage, current, and temperature of the battery under the constant charging data, evaluating the battery charging efficiency, and promptly issuing warnings when abnormalities are found; based on the evaluated battery charging efficiency, constructing a battery charging scheduling model for the charging station, and solving the global optimal charging power allocation plan when charging multiple batteries.

[0117] Furthermore, the electronic device may also include a user interface. Of course, the architecture of the present invention is only exemplary, and when implementing different devices, one or more components in the electronic device disclosed in the present invention may be omitted according to actual needs.

[0118] Example 4

[0119] The invention also discloses a computer-readable storage medium.

[0120] The computer-readable storage medium has computer-readable instructions stored thereon.

[0121] When the computer-readable instructions are executed by the processor, the intelligent charging and discharging optimization method for the battery of a new energy vehicle charging station disclosed in the present application can be executed.

[0122] The storage medium includes, but is not limited to, for example, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. In addition, according to an embodiment of the present invention, the process described above with reference to the flowchart may be implemented as a computer software program.

[0123] For example, the present invention provides a non-temporary machine-readable storage medium, which stores machine-readable instructions, and the machine-readable instructions can be run by a processor to execute instructions corresponding to the method steps provided by the present invention, for example: constructing a charging demand prediction model based on a long-short term neural network (LSTM) model, using historical charging data, and combining date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in future time periods; based on the charging demand of new energy vehicles in future time periods, constructing multi-type optimized charging and discharging strategies for new energy vehicle charging stations, and respectively constructing peak shaving and valley filling strategies and new energy consumption strategies; constructing a battery charging efficiency evaluation model, charging the battery using constant charging data over a period of time, collecting data on the voltage, current, and temperature of the battery under constant charging data, evaluating the battery charging efficiency, and promptly issuing warnings when abnormalities are found; based on the evaluated battery charging efficiency, constructing a battery charging scheduling model for the charging station, and solving the globally optimal charging power allocation plan when charging multiple batteries.

[0124] When the computer program is executed by the central processing unit (CPU), the above functions defined in the method of the present invention are performed. The method, apparatus, and device of the present invention may be implemented in many ways. For example, the method, apparatus, and device of the present invention may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware.

[0125] The above sequence for the steps of the method is for illustration only, and the steps of the method of the present invention are not limited to the sequence specifically described above unless otherwise specifically stated.

[0126] In addition, in some embodiments, the present invention can also be implemented as a program recorded in a recording medium, which includes machine-readable instructions for implementing the method according to the present invention. Therefore, the present invention also covers a recording medium storing a program for executing the method according to the present invention.

[0127] In addition, the parts of the above technical solutions provided in the embodiments of the present invention that are consistent with the implementation principles of the corresponding technical solutions in the prior art are not described in detail to avoid excessive redundancy.

[0128] The specific implementation modes as described above further describe the purpose, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation mode of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for optimizing the intelligent charging and discharging of batteries in a new energy vehicle charging station, characterized in that: include: Construct a charging demand prediction model based on the long-term and short-term neural network LSTM model, use historical charging data, and combine date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future time period; Based on the charging demand of new energy vehicles in the future time period, build multi-type optimized charging and discharging strategies for new energy vehicle charging stations, and respectively build peak shaving and valley filling strategies and new energy consumption strategies; Construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect the battery's voltage, current, and temperature data under constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found; Based on the evaluated battery charging efficiency, a battery charging scheduling model for the charging station is constructed to solve the global optimal charging power allocation scheme when charging multiple batteries.

2. The intelligent charging and discharging optimization method for new energy vehicle charging station batteries according to claim 1 is characterized in that: Acquire historical charging data, including N charging records, wherein the charging records include information on real-time charging power consumption, peak charging power consumption, and charging power, and integrate the charging records into a data set; Collect date type and weather data related to charging demand and merge them with the charging record dataset to form a feature dataset; Construct a long-term and short-term neural network LSTM model as a charging demand prediction model; define the output layer of the LSTM model, set the output dimension to the length p of the future time period, and select the mean square error MSE as the loss function; The feature data set X and the corresponding charging demand value Y are divided into a training set and a test set according to a predetermined ratio, the LSTM model is trained using the training set data, and the model parameters are updated through the back propagation algorithm and optimizer; Use the test set data to evaluate the trained LSTM model and calculate the prediction performance indicators of the model on the test set; adjust the model's hyperparameters based on the model's prediction performance, and repeat the training and evaluation process until the prediction performance indicators meet the requirements; Based on the trained charging demand prediction model, charging demand prediction is performed for a given future time period T = t1, t2, ..., t p , construct feature vectors of date type and weather factors As the input of the charging demand prediction model; t p is the tth time period in the future p A point in time, For the tth p The feature vectors of date type and weather factors at each time point; Using the trained charging demand prediction model, the feature vector X T Make predictions to obtain the charging demand forecast for the future time period in Indicates the tth time period in the future p The predicted value of charging demand at a time point; The charging demand forecast value Output serves as input for subsequent optimization of charging and discharging strategies.

3. The intelligent charging and discharging optimization method for batteries in new energy vehicle charging stations according to claim 2 is characterized in that: Construct multiple types of optimized charging and discharging strategies for new energy vehicle charging stations, including peak load shaving and valley filling strategies and new energy consumption strategies; The objective function of the peak-shaving and valley-filling strategy is defined to minimize the charging load of the charging station during peak hours and maximize the charging amount during valley hours. The objective function of the peak-shaving and valley-filling strategy is expressed as: Where t is the time in the future time period T, T peak represents the set of peak hours, T valley represents the set of low periods, P t represents the charging power at future time t; The constraints of the peak-shaving and valley-filling strategy include: charging power constraint: 0≤P t ≤P max , And the charge capacity constraint:∑ t∈T P t Δt=E demand ; Among them, P max represents the maximum charging power of the charging station, T represents the future time period set, Δt represents the time interval, and E demand Represents the predicted total charging demand.

4. The intelligent charging and discharging optimization method for new energy vehicle charging station batteries according to claim 3 is characterized in that: Define the objective function of the new energy consumption strategy, the goal is to maximize the amount of renewable energy consumed by the charging station; the objective function of the new energy consumption strategy is expressed as: maxΣ t∈ TP t ·R t , where R t It represents the proportion of renewable energy power generation in the total power generation at the future time t; The constraints of the new energy consumption strategy include charging power constraints: 0≤P t ≤P max , Charge capacity constraint: ∑ t∈T P t Δt=E demand , and renewable energy generation constraint: 0≤R t ≤1, 5. The intelligent charging and discharging optimization method for batteries in new energy vehicle charging stations according to claim 4 is characterized in that: The objective functions of the peak-shaving and valley-filling strategy and the new energy consumption strategy are weighted and summed by the weighted summation method to construct a multi-objective optimization problem. The objective function after weighted summation is as follows: Among them, λ1 and λ2 are the weight coefficients of the peak shaving and valley filling strategy and the new energy consumption strategy respectively; The constraints for solving the multi-objective optimization include the charging power constraint: 0≤P t ≤P max , Charge capacity constraint: ∑ t∈T P t Δt=E demand ;as well as Renewable energy generation capacity constraint: 0≤R t ≤1, 6. The intelligent charging and discharging optimization method for batteries in a new energy vehicle charging station according to claim 5 is characterized in that: The battery is charged constantly for a period of time, and the voltage, current and temperature parameters of the charging battery are collected at fixed time intervals Δt during the constant charging process; A charging efficiency scoring index S is defined to evaluate the charging efficiency of the battery; the value range of the charging efficiency scoring index S is [0,1], where S=1 indicates that the battery fully receives a constant charging output; S=0 indicates that the battery does not receive any charging output at all; Calculate the cumulative charging output Q during constant charging out And the battery cumulative reception Q in : Among them I out,t is the constant charging output current at time t; Δt is the time interval; K is the total number of sampling points; Among them I in,t is the actual received current of the battery at time t; Calculate the charging efficiency η, that is, the cumulative amount of battery received Q in and the accumulated charging output Q out Ratio of: Constructing the battery charging efficiency threshold η th , when η≥η th When η<η, it indicates that the battery charging efficiency is qualified; when η<η th , it indicates that the battery charging efficiency is unqualified, and a charging warning reminder is issued for the battery.

7. The intelligent charging and discharging optimization method for batteries in a new energy vehicle charging station according to claim 6 is characterized in that: The objective function of the battery charging scheduling model of the charging station is defined to maximize the weighted total charging efficiency score while satisfying the load constraints of the charging station: Where M is the total number of batteries in the charging station; w j is the weight coefficient of the jth battery; S j is the charging efficiency score index of the jth battery; P j is the charging power allocated to the jth battery; The constraints of the battery charging scheduling model for charging stations include: Charging station load constraints: Where P station (t) is the maximum available charging power of the charging station at time t; Battery charging power constraint: 0≤P j ≤P j,max , Where P j,max is the maximum charging power of the jth battery; battery charging efficiency constraint: S j ≥S th , Where S th is the threshold value of the battery charging efficiency score indicator; Solve the battery charging scheduling model of the charging station and transform the optimization problem into a linear programming problem: The linear programming algorithm is used to solve the linear programming problem and the optimal solution P is obtained. best =[P′1,P'2...,P' M ], where P' M represents the optimal charging power allocated to the Mth battery; When the charging station is not fully loaded, Do not allocate charging power to the rechargeable battery. represents the optimal charging power assigned to the jth battery, j∈[1,M]; When the charging station is fully loaded, According to the optimal charging power allocation scheme P best Allocate charging power to each battery.

8. A new energy vehicle charging station battery intelligent charging and discharging optimization system, which is used to implement the new energy vehicle charging station battery intelligent charging and discharging optimization method according to any one of claims 1 to 7, characterized in that: include: Charging demand prediction module, multi-type optimization module, charging efficiency evaluation module and charging scheduling module; The charging demand prediction module is used to build a charging demand prediction model based on the long-term and short-term neural network LSTM model, using historical charging data and combining date and weather factors to establish a charging demand prediction model to predict the charging demand of new energy vehicles in the future time period; The multi-type optimization module constructs a multi-type optimization charging and discharging strategy for new energy vehicle charging stations based on the charging demand of new energy vehicles in the future time period, and constructs a peak shaving and valley filling strategy and a new energy consumption strategy respectively; The charging efficiency evaluation module is used to construct a battery charging efficiency evaluation model, charge the battery using constant charging data over a period of time, collect data on the voltage, current, and temperature of the battery under the constant charging data, evaluate the battery charging efficiency, and issue a timely warning when an abnormality is found; The charging scheduling module constructs a battery charging scheduling model for the charging station based on the evaluated battery charging efficiency, and solves a globally optimal charging power allocation scheme when charging multiple batteries.

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