New energy vehicle charging management method and system based on artificial intelligence

Abstract

The present invention provides a new energy vehicle charging management method and system based on artificial intelligence. The method includes obtaining historical vehicle information and driving road network information of historical new energy vehicles, determining the total vehicle charging load based on the historical vehicle information and driving road network information; determining a time load diagram based on the total vehicle charging load and a preset period, dividing the preset period into several time zones, and determining the final charging price corresponding to each time zone based on the time load diagram; constructing a charging optimization management model based on the final charging price; using an improved particle swarm optimization algorithm to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone; charging the new energy vehicle to be charged based on the final charging power to realize the charging management process. The present invention can reduce the supply and demand power deviation in different time zones, reduce the burden on charging stations and power grids, and also improve the guidance effect of orderly charging.

Description

Technical Field

[0001] The present invention belongs to the technical field of charging management, and specifically relates to a new energy vehicle charging management method and system based on artificial intelligence. Background Art

[0002] The charging process of new energy vehicles in the existing technology is usually a large-scale disordered charging process, which leads to several new grid load peaks in the actual charging process. In addition, for the traditional charging time-of-use pricing process, the time of the charging peak and the charging trough is unchanged, which leads to great limitations in the actual application of charging piles, aggravating the disorder of new energy vehicle charging while also increasing the load on the power grid and charging stations, making it difficult to guide the orderly charging process of new energy vehicle users. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an artificial intelligence-based new energy vehicle charging management method and system to solve the technical problems in the prior art.

[0004] In one aspect, the present invention provides the following technical solution: a new energy vehicle charging management method based on artificial intelligence, comprising:

[0005] Obtain historical vehicle information and road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the road network information;

[0006] Determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each time zone based on the time load diagram;

[0007] Building a charging optimization management model based on the final charging price;

[0008] An improved particle swarm optimization algorithm is used to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone;

[0009] The new energy vehicle to be charged is charged based on the final charging power to implement a charging management process.

[0010] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention first obtains historical vehicle information and driving road network information of historical new energy vehicles, and determines the total charging load of the vehicle based on the historical vehicle information and driving road network information; then determines a time load diagram based on the total charging load of the vehicle and a preset period, divides the preset period into several time zones, and determines the final charging price corresponding to each time zone based on the time load diagram; then constructs a charging optimization management model based on the final charging price; then adopts an improved particle swarm optimization algorithm to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone; finally, charges the new energy vehicle to be charged based on the final charging power to realize the charging management process. The present invention first determines the total charging load of the vehicle and uses it to determine the final charging price corresponding to each time zone, thereby reducing the supply and demand power deviation in different time zones, reducing the burden on charging stations and power grids, and also improving the guiding effect of orderly charging. Finally, the final charging power is determined, which can further reduce the difference between charging peaks and charging valleys and improve the guiding effect of orderly charging.

[0011] Preferably, the step of determining the total vehicle charging load based on the historical vehicle information and the driving road network information includes:

[0012] Extracting original traffic flow data and a set of factors influencing the traffic flow data from the historical vehicle information, and decomposing the original traffic flow data to obtain a plurality of decomposition components;

[0013] Performing feature selection and feature integration on the vehicle flow data influencing factor set and the plurality of decomposition components to obtain a selected feature set;

[0014] Obtaining training vehicle flow data, and inputting the training vehicle flow data into a preset vehicle flow prediction model for prediction to obtain predicted vehicle flow data;

[0015] Determine the historical driving speed and driving power consumption of new energy vehicles based on the predicted traffic flow data;

[0016] The vehicle SOC is determined based on the initial power and driving power consumption of historical new energy vehicles, and the vehicle probability is calculated based on the vehicle SOC. :

[0017] ;

[0018] Where, Indicates the The SOC of the last trip of a new energy vehicle in history, Indicates the The standard deviation of the SOC of historical new energy vehicles, Indicates the The expected vehicle SOC of a historical new energy vehicle;

[0019] The historical new energy vehicles with a probability greater than the preset probability are regarded as the main new energy vehicles, and the path planning algorithm is used to determine the driving path of the main new energy vehicles to the target charging station;

[0020] The path power consumption of the main new energy vehicles on the driving route is determined based on the driving speed of the main new energy vehicles, and the individual charging load of the main new energy vehicles is determined based on the path power consumption. The individual charging loads of all the main new energy vehicles in the same charging time period are superimposed in sequence to obtain the total vehicle charging load.

[0021] Preferably, the step of determining the final charging price corresponding to each time zone based on the time load graph includes:

[0022] Identify the maximum total vehicle charging load and the minimum total vehicle charging load in the time load diagram, and use the time zone corresponding to the maximum total vehicle charging load as the peak time zone and the time zone corresponding to the total vehicle charging load as the trough time zone. Total load with minimum car charging Calculate the first standard value With the second standard value :

[0023] ; ;

[0024] Where, Indicates the The total vehicle charging load corresponding to each time zone;

[0025] Based on the first standard value With the second standard value Calculate similarity matrix :

[0026] ;

[0027] Where, Represents similarity matrix Middle Row, No. Elements of the row, is the adjustment coefficient, is the number of time zones;

[0028] For the similarity matrix Perform several quadratic solutions until , if the matrix Elements in If it is greater than the classification threshold, the time zone and time zone Belonging to the same category, the time zone that belongs to the same category as the peak time zone is taken as the peak time zone, the time zone that belongs to the same category as the trough time zone is taken as the trough time zone, and the other time zones are taken as the flat time zones, so as to obtain a set of classified time zones;

[0029] Performing fitting adjustment on the time load graph based on the set of classified time zones to obtain an adjusted time load graph;

[0030] A final charging price corresponding to each of the time zones is determined based on the adjusted time load map.

[0031] Preferably, the step of determining the final charging price corresponding to each time zone based on the adjusted time load graph includes:

[0032] Constructing a price objective function based on the adjustment time load diagram :

[0033] ;

[0034] ；

[0035] ；

[0036] ；

[0037] ;

[0038] Where, Represent the first, second, third, and fourth function values ​​respectively, 、 、 、 Respectively represent The electricity purchase cost price corresponding to each time zone, the charging price to be solved, the lower limit of the price fluctuation range, and the lower limit of the price fluctuation range. 、 Represents the time load diagram, the adjusted time load diagram, and The load corresponding to each time zone, Indicates the time load diagram and the adjustment time load diagram The difference between the loads corresponding to the time zones, represents the price volatility factor, represents the standard deviation of the charging price to be solved, Indicates the The price discount coefficient of each new energy vehicle to be charged, Indicates the The ideal charging price for each new energy vehicle to be charged. Indicates the user's sensitivity to charging prices, 、 Respectively represent New energy vehicles waiting to be charged are Remaining power and ideal power in each time zone, Indicates the The response coefficient of the user to the charging power of each new energy vehicle to be charged, They represent the number of time zones and the number of new energy vehicles to be charged respectively;

[0039] The objective function is solved by genetic algorithm The charging price to be solved in Solve to output the final charging price corresponding to each time zone.

[0040] Preferably, in the step of constructing a charging optimization management model based on the final charging price, the charging optimization management model is:

[0041] ;

[0042] Where, 、 Represent the first and second objective function values ​​respectively, Indicates the The load power corresponding to each time zone is: Indicates the average load power, Indicates the New energy vehicles waiting to be charged are Charging power in each time zone, Respectively represent the number of time zones and the number of new energy vehicles to be charged. Indicates the number of charging states, Indicates the The final charging price corresponding to the time zone, Indicates the length of a single time zone. Indicates the The battery life of a new energy vehicle to be charged, Indicates the The maximum total battery capacity of each new energy vehicle to be charged, Indicates the linear coefficient between battery life and charge and discharge cycle number, Indicates the New energy vehicles waiting to be charged are SOC of each time zone.

[0043] Preferably, the step of solving the charging optimization management model using an improved particle swarm optimization algorithm to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone includes:

[0044] Determine a number of particles according to the charging optimization management model, initialize the position and velocity of each particle, and calculate the fitness of each particle;

[0045] Iteratively update the local optimal solution and the global optimal solution according to the fitness of the particles;

[0046] Calculate and update inertia weight :

[0047] ;

[0048] Where, Represent the maximum and minimum set weights respectively, 、 、 Respectively represent the particle fitness of the current iteration number, the average particle fitness, and the minimum particle fitness, Respectively represent the current number of iterations and the maximum number of iterations, represents the decay constant;

[0049] Update the inertia weight based on Determine the final charging power of each new energy vehicle to be charged in the corresponding time zone.

[0050] Preferably, the inertia weight is updated based on The steps of determining the final charging power of each new energy vehicle to be charged in the corresponding time zone include:

[0051] Update the inertia weight based on the , the local optimal solution of the current iteration number and the global optimal solution update the position and velocity of the particle:

[0052] ;

[0053] ;

[0054] Where, 、 Respectively represent 、 The velocity of the particle after iterations, Respectively represent 、 The position of the particle after iterations, Represent the first and second learning rates respectively, Represent the first and second random numbers respectively, Respectively represent 、 Local optimal solution and global optimal solution after iterations;

[0055] Iterate and repeat the process of updating the particle's position and velocity until the stopping condition is met, and output the final global optimal solution;

[0056] The final global optimal solution is iteratively disturbed and the new solution is accepted using an annealing algorithm to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone.

[0057] In a second aspect, the present invention provides the following technical solution: an artificial intelligence-based new energy vehicle charging management system, the system comprising:

[0058] A load module is used to obtain historical vehicle information and road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the road network information;

[0059] a pricing module, configured to determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each of the time zones based on the time load diagram;

[0060] an optimization module, configured to construct a charging optimization management model based on the final charging price;

[0061] A solution module, configured to solve the charging optimization management model using an improved particle swarm optimization algorithm to obtain a final charging power of each new energy vehicle to be charged in a corresponding time zone;

[0062] A management module is used to charge the new energy vehicle to be charged based on the final charging power to implement a charging management process.

[0063] In a third aspect, the present invention provides the following technical solution: a computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the computer program, the new energy vehicle charging management method based on artificial intelligence as described above is implemented.

[0064] In a fourth aspect, the present invention provides the following technical solution: a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned artificial intelligence-based new energy vehicle charging management method. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0066] Figure 1 This is a flow chart of the new energy vehicle charging management method based on artificial intelligence provided in Example 1 of the present invention;

[0067] Figure 2 This is a structural block diagram of the new energy vehicle charging management system based on artificial intelligence provided in the second embodiment of the present invention;

[0068] Figure 3 A schematic diagram of the hardware structure of a computer provided in another embodiment of the present invention.

[0069] The embodiments of the present invention will be further described below with reference to the accompanying drawings. DETAILED DESCRIPTION

[0070] The following describes embodiments of the present invention in detail, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the embodiments of the present invention, and should not be construed as limiting the present invention.

[0071] In the description of the embodiments of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0072] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of the technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, "plurality" means two or more, unless otherwise specifically defined.

[0073] In the embodiments of the present invention, unless otherwise expressly specified or limited, the terms "installed," "connected," "connected," "fixed," etc. should be understood in a broad sense. For example, they may refer to fixed connection, detachable connection, or integration; mechanical connection or electrical connection; direct connection or indirect connection through an intermediate medium; internal communication between two components or interaction between two components. Those skilled in the art will understand the specific meanings of the above terms in the embodiments of the present invention based on specific circumstances.

[0074] Example 1

[0075] In the first embodiment of the present invention, Figure 1 As shown, a new energy vehicle charging management method based on artificial intelligence includes:

[0076] S1. Obtain historical vehicle information and road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the road network information;

[0077] Specifically, the historical vehicle information here includes the speed distribution and flow changes of the traffic flow. The driving road network information specifically determines a preset range with the charging station as the center, and then determines a number of network structures with a topological structure in a grid manner, which includes a number of road network nodes and the connection relationship between the road network nodes.

[0078] Wherein, the step S1 includes:

[0079] S11, extracting original traffic flow data and a set of factors influencing the traffic flow data from the historical vehicle information, and decomposing the original traffic flow data to obtain a plurality of decomposition components;

[0080] Specifically, wavelet decomposition is used here to decompose the original traffic flow data.

[0081] S12, performing feature selection and feature integration on the vehicle flow data influencing factor set and the plurality of decomposition components to obtain a selected feature set;

[0082] Specifically, here we use the mRMR-based feature information selection algorithm to select the factors and decomposition components that have a great impact on the traffic flow data.

[0083] S13, obtaining training vehicle flow data, inputting the training vehicle flow data into a preset vehicle flow prediction model for prediction, and obtaining predicted vehicle flow data;

[0084] Specifically, the preset traffic flow prediction model here is the LSTM model.

[0085] S14, determining the historical driving speed and driving power consumption of new energy vehicles based on the predicted traffic flow data;

[0086] Specifically, the driving speed here for:

[0087] ;

[0088] Where, For road sections in the road network The free flow speed, For road sections in the road network of traffic volume, For road sections in the road network The traffic capacity, is the road adjustment factor, which is determined according to different types of road sections;

[0089] The driving power consumption is determined based on the driving speed, driving time and power consumption per unit distance.

[0090] S15, determining the vehicle SOC based on the initial power and driving power consumption of the historical new energy vehicle, and calculating the vehicle probability based on the vehicle SOC :

[0091] ;

[0092] Where, Indicates the The SOC of the last trip of a new energy vehicle in history, Indicates the The standard deviation of the SOC of historical new energy vehicles, Indicates the The expected vehicle SOC of a historical new energy vehicle.

[0093] S16. Determine the historical new energy vehicle with a probability greater than a preset probability as the primary new energy vehicle, and use a path planning algorithm to determine a driving path for the primary new energy vehicle to the target charging station.

[0094] Specifically, when the probability of a car is greater than a preset probability, it indicates that the vehicle is more likely to go to a charging station to perform a charging process. The path planning algorithm here is specifically the Floyd algorithm.

[0095] S17. Determine the path power consumption of the main new energy vehicle on the driving path based on the driving speed of the main new energy vehicle, determine the individual charging load of the main new energy vehicle based on the path power consumption, and sequentially add the individual charging loads of all the main new energy vehicles in the same charging time period to obtain a total vehicle charging load;

[0096] Specifically, the single charging load can be determined based on the sum of driving power consumption and path power consumption, and then superimposed according to different time periods to obtain the total vehicle charging load.

[0097] S2. Determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each time zone based on the time load diagram;

[0098] Wherein, the step S2 includes:

[0099] S21, identifying the maximum total vehicle charging load and the minimum total vehicle charging load in the time load diagram, and taking the time zone corresponding to the maximum total vehicle charging load as the peak time zone and the time zone corresponding to the total vehicle charging load as the trough time zone, based on the maximum total vehicle charging load Total load with minimum car charging Calculate the first standard value With the second standard value :

[0100] ; ;

[0101] Where, Indicates the The total vehicle charging load corresponding to each time zone.

[0102] S22, based on the first standard value With the second standard value Calculate similarity matrix :

[0103] ;

[0104] Where, Represents similarity matrix Middle Row, No. Elements of the row, is the adjustment coefficient, is the number of time zones.

[0105] S23, the similarity matrix Perform several quadratic solutions until , if the matrix Elements in If it is greater than the classification threshold, the time zone and time zone Belonging to the same category, the time zone that belongs to the same category as the peak time zone is taken as the peak time zone, the time zone that belongs to the same category as the trough time zone is taken as the trough time zone, and the other time zones are taken as the flat time zones, so as to obtain a set of classified time zones;

[0106] Specifically, if the element Greater than the classification threshold, time zone and time zone Belong to the same category, therefore, all time zones are judged with the peak time zone and the trough time zone respectively to see whether they are in the same category, so that several peak time zones and trough time zones can be obtained, and then the remaining time zones are regarded as flat time zones to obtain a time zone set.

[0107] S24. Perform fitting adjustment on the time load graph based on the classified time zone set to obtain an adjusted time load graph;

[0108] Specifically, the time load graph is a continuous curve with certain errors. By determining different types of time zones, the increasing and decreasing relationship in the curve can be adjusted and fitted to obtain an adjusted time load graph.

[0109] S25. Determine a final charging price corresponding to each time zone based on the adjusted time load graph;

[0110] Wherein, the step S25 includes:

[0111] S251: Constructing a price target function based on the adjustment time load diagram :

[0112] ;

[0113] ；

[0114] ；

[0115] ；

[0116] ;

[0117] Where, Represent the first, second, third, and fourth function values ​​respectively, 、 、 、 Respectively represent The electricity purchase cost price corresponding to each time zone, the charging price to be solved, the lower limit of the price fluctuation range, and the lower limit of the price fluctuation range. 、 Represents the time load diagram, the adjusted time load diagram, and The load corresponding to each time zone, Indicates the time load diagram and the adjustment time load diagram The difference between the loads corresponding to the time zones, represents the price volatility factor, represents the standard deviation of the charging price to be solved, Indicates the The price discount coefficient of each new energy vehicle to be charged, Indicates the The ideal charging price for each new energy vehicle to be charged. Indicates the user's sensitivity to charging prices, 、 Respectively represent New energy vehicles waiting to be charged are Remaining power and ideal power in each time zone, Indicates the The response coefficient of the user to the charging power of each new energy vehicle to be charged, They represent the number of time zones and the number of new energy vehicles to be charged respectively;

[0118] Specifically, for the price objective function In terms of charging, it fully considers the impact of different prices on users, the cost of purchasing electricity from the power grid, the deviation between supply and demand, and the benefits of users and charging stations.

[0119] S252, using genetic algorithm to solve the objective function The charging price to be solved in Solving the problem to output the final charging price corresponding to each time zone;

[0120] Specifically, the genetic algorithm is a commonly used algorithm in the prior art, and therefore will not be described in detail here.

[0121] S3. Building a charging optimization management model based on the final charging price;

[0122] The charging optimization management model is:

[0123] ;

[0124] Where, 、 Represent the first and second objective function values ​​respectively, Indicates the The load power corresponding to each time zone is: Indicates the average load power, Indicates the New energy vehicles waiting to be charged are Charging power in each time zone, Respectively represent the number of time zones and the number of new energy vehicles to be charged. Indicates the number of charging states, Indicates the The final charging price corresponding to the time zone, Indicates the length of time in a single time zone. Indicates the The battery life of a new energy vehicle to be charged, Indicates the The maximum total battery capacity of each new energy vehicle to be charged, Indicates the linear coefficient between battery life and charge and discharge cycle number, Indicates the New energy vehicles waiting to be charged are SOC of each time zone;

[0125] Specifically, the goal of the charging optimization management model is to minimize the mean square error of the grid load and minimize the user's charging cost, while considering the impact of charging power on battery life and vehicle efficiency.

[0126] S4. Solving the charging optimization management model using an improved particle swarm optimization algorithm to obtain a final charging power for each new energy vehicle to be charged in a corresponding time zone;

[0127] Wherein, the step S4 includes:

[0128] S41. Determine a number of particles according to the charging optimization management model, initialize the position and velocity of each particle, and calculate the fitness of each particle.

[0129] S42. Iteratively update the local optimal solution and the global optimal solution according to the fitness of the particles.

[0130] S43. Calculate and update inertia weight :

[0131] ;

[0132] Where, Represent the maximum and minimum set weights respectively, 、 、 Respectively represent the particle fitness of the current iteration number, the average particle fitness, and the minimum particle fitness, Respectively represent the current number of iterations and the maximum number of iterations, represents the decay constant;

[0133] Specifically, the above steps are the steps of the particle swarm algorithm in the prior art, but the difference is that in the present application, the inertia weight is adjusted accordingly to achieve an adaptive balance between global search and local search, thereby effectively shortening the number of iterations required for problem solving, thereby improving the calculation speed and convergence speed, and at the same time solving the problem of local optimization of particles. In actual situations, the particles may already be the optimal part of the current local area at the initial iteration. Since the initial value of the inertia weight is the largest and continues to decrease linearly, the local search is continuously strengthened and eventually the particles fall into the local optimum. Therefore, the present invention fully considers the influence of fitness on optimization, and can expand the range of outward search of particles, thereby avoiding falling into the local optimization situation.

[0134] S44, based on the updated inertia weight Determine the final charging power of each new energy vehicle to be charged in the corresponding time zone;

[0135] Wherein, the step S44 includes:

[0136] S441, based on the updated inertia weight , the local optimal solution of the current iteration number and the global optimal solution update the position and velocity of the particle:

[0137] ;

[0138] ;

[0139] Where, 、 Respectively represent 、 The velocity of the particle after iterations, Respectively represent 、 The position of the particle after iterations, Represent the first and second learning rates respectively, Represent the first and second random numbers respectively, Respectively represent 、 The local optimal solution and the global optimal solution after iterations.

[0140] S442, iteratively repeat the process of updating the particle position and velocity until the stopping condition is met, and output the final global optimal solution;

[0141] Specifically, the above process is a common usage of the particle swarm algorithm, except that the inertia weights used are different.

[0142] S443. Using an annealing algorithm, iteratively perturb the final global optimal solution and accept the new solution to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone;

[0143] Specifically, the annealing algorithm, namely the SA algorithm, is a commonly used algorithm in the prior art, so it will not be described in detail here. The efficiency and accuracy of the solution can be enhanced by the annealing algorithm, and the solution sought here is to find the optimal .

[0144] S5. Charging the new energy vehicle to be charged based on the final charging power to implement a charging management process;

[0145] Specifically, the corresponding final charging power is used to charge the new energy vehicles to realize the charging management process, reduce the deviation of power supply and demand in different time zones, reduce the burden on charging stations and power grids, and also improve the guidance effect of orderly charging.

[0146] The first embodiment of the present invention provides an artificial intelligence-based new energy vehicle charging management method. The method first obtains historical vehicle information and road network information of historical new energy vehicles, and determines the total vehicle charging load based on the historical vehicle information and road network information. Then, a time load diagram is determined based on the total vehicle charging load and a preset period. The preset period is divided into several time zones, and the final charging price corresponding to each time zone is determined based on the time load diagram. Then, a charging optimization management model is constructed based on the final charging prices. Then, an improved particle swarm optimization algorithm is used to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone. Finally, the new energy vehicle to be charged is charged based on the final charging power to implement the charging management process. The present invention first determines the total vehicle charging load and uses the final charging price corresponding to each time zone based on the total vehicle charging load. This can reduce the supply and demand power deviation in different time zones, reduce the burden on charging stations and the power grid, and improve the guidance effect of orderly charging. Finally, the final charging power is determined, which can further reduce the difference between charging peaks and charging valleys and improve the guidance effect of orderly charging.

[0147] Example 2

[0148] like Figure 2 As shown, in the second embodiment of the present invention, a new energy vehicle charging management system based on artificial intelligence is provided, and the system includes:

[0149] Load module 1, used to obtain historical vehicle information and travel road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the travel road network information;

[0150] A pricing module 2 is configured to determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each time zone based on the time load diagram;

[0151] Optimization module 3, configured to construct a charging optimization management model based on the final charging price;

[0152] Solving module 4, for solving the charging optimization management model using an improved particle swarm optimization algorithm to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone;

[0153] A management module 5 is configured to charge the new energy vehicle to be charged based on the final charging power to implement a charging management process;

[0154] The load module 1 comprises:

[0155] a decomposition submodule, configured to extract original traffic flow data and a set of factors influencing traffic flow data from the historical vehicle information, and decompose the original traffic flow data to obtain a plurality of decomposition components;

[0156] an integration submodule, configured to perform feature selection and feature integration on the vehicle flow data influencing factor set and the plurality of decomposition components to obtain a selected feature set;

[0157] The prediction submodule is used to obtain training vehicle flow data, input the training vehicle flow data into a preset vehicle flow prediction model for prediction, and obtain predicted vehicle flow data;

[0158] A power consumption submodule, configured to determine the historical driving speed and power consumption of new energy vehicles based on the predicted traffic flow data;

[0159] The probability submodule is used to determine the vehicle SOC based on the initial power and driving power consumption of historical new energy vehicles, and calculate the vehicle probability based on the vehicle SOC :

[0160] ;

[0161] Where, Indicates the The SOC of the last trip of a new energy vehicle in history, Indicates the The standard deviation of the SOC of historical new energy vehicles, Indicates the The expected vehicle SOC of a historical new energy vehicle;

[0162] The path submodule is used to take the historical new energy vehicles with a probability greater than a preset probability as the main new energy vehicles, and use the path planning algorithm to determine the driving path of the main new energy vehicles to the target charging station;

[0163] The superposition submodule is used to determine the path power consumption of the main new energy vehicles on the driving path based on the driving speed of the main new energy vehicles, determine the individual charging load of the main new energy vehicles based on the path power consumption, and superimpose the individual charging loads of all the main new energy vehicles in the same charging time period in sequence to obtain the total vehicle charging load.

[0164] The price module 2 includes:

[0165] The identification submodule is used to identify the maximum total vehicle charging load and the minimum total vehicle charging load in the time load diagram, and use the time zone corresponding to the maximum total vehicle charging load as the peak time zone and the time zone corresponding to the total vehicle charging load as the trough time zone. Total load with minimum car charging Calculate the first standard value With the second standard value :

[0166] ; ;

[0167] Where, Indicates the The total vehicle charging load corresponding to each time zone;

[0168] Matrix submodule for the first standard value With the second standard value Calculate similarity matrix :

[0169] ;

[0170] Where, Represents similarity matrix Middle Row, No. Elements of the row, is the adjustment coefficient, is the number of time zones;

[0171] The classification submodule is used to classify the similarity matrix Perform several quadratic solutions until , if the matrix Elements in If it is greater than the classification threshold, the time zone and time zone Belonging to the same category, the time zone that belongs to the same category as the peak time zone is taken as the peak time zone, the time zone that belongs to the same category as the trough time zone is taken as the trough time zone, and the other time zones are taken as the flat time zones, so as to obtain a set of classified time zones;

[0172] a fitting submodule, configured to perform fitting adjustment on the time load graph based on the set of classified time zones to obtain an adjusted time load graph;

[0173] The price output submodule is used to determine the final charging price corresponding to each time zone based on the adjusted time load diagram.

[0174] The price output submodule includes:

[0175] An objective function unit is used to construct a price objective function based on the adjustment time load diagram :

[0176] ;

[0177] ；

[0178] ；

[0179] ；

[0180] ;

[0181] Where, Represent the first, second, third, and fourth function values ​​respectively, 、 、 、 Respectively represent The electricity purchase cost price corresponding to each time zone, the charging price to be solved, the lower limit of the price fluctuation range, and the lower limit of the price fluctuation range. 、 Represents the time load diagram, the adjusted time load diagram, and The load corresponding to each time zone, Indicates the time load diagram and the adjustment time load diagram The difference between the loads corresponding to the time zones, represents the price volatility factor, represents the standard deviation of the charging price to be solved, Indicates the The price discount coefficient of each new energy vehicle to be charged, Indicates the The ideal charging price for each new energy vehicle to be charged. Indicates the user's sensitivity to charging prices, 、 Respectively represent New energy vehicles waiting to be charged are Remaining power and ideal power in each time zone, Indicates the The response coefficient of the user to the charging power of each new energy vehicle to be charged, They represent the number of time zones and the number of new energy vehicles to be charged respectively;

[0182] Function solving unit, used to solve the target function using genetic algorithm The charging price to be solved in Solve to output the final charging price corresponding to each time zone.

[0183] The charging optimization management model is:

[0184] ;

[0185] Where, 、 Represent the first and second objective function values ​​respectively, Indicates the The load power corresponding to each time zone is: Indicates the average load power, Indicates the New energy vehicles waiting to be charged are Charging power in each time zone, Respectively represent the number of time zones and the number of new energy vehicles to be charged. Indicates the number of charging states, Indicates the The final charging price corresponding to the time zone, Indicates the length of a single time zone. Indicates the The battery life of a new energy vehicle to be charged, Indicates the The maximum total battery capacity of each new energy vehicle to be charged, Indicates the linear coefficient between battery life and charge and discharge cycle number, Indicates the New energy vehicles waiting to be charged are SOC of each time zone.

[0186] The solution module 4 includes:

[0187] a fitness submodule, configured to determine a number of particles according to the charging optimization management model, initialize the position and velocity of each particle, and calculate the fitness of each particle;

[0188] The update submodule is used to iteratively update the local optimal solution and the global optimal solution according to the fitness of the particles;

[0189] Weight submodule, used to calculate and update inertia weight :

[0190] ;

[0191] Where, Represent the maximum and minimum set weights respectively, 、 、 Respectively represent the particle fitness of the current iteration number, the average particle fitness, and the minimum particle fitness, Respectively represent the current number of iterations and the maximum number of iterations, represents the decay constant;

[0192] A power output submodule is configured to update the inertia weight based on the Determine the final charging power of each new energy vehicle to be charged in the corresponding time zone.

[0193] The power output submodule includes:

[0194] A position and velocity updating unit for updating the inertia weight based on the , the local optimal solution of the current iteration number and the global optimal solution update the position and velocity of the particle:

[0195] ;

[0196] ;

[0197] Where, 、 Respectively represent 、 The velocity of the particle after iterations, Respectively represent 、 The position of the particle after iterations, Represent the first and second learning rates respectively, Represent the first and second random numbers respectively, Respectively represent 、 Local optimal solution and global optimal solution after iterations;

[0198] The output unit is used to iteratively repeat the update process of the particle position and velocity until the stopping condition is met and output the final global optimal solution;

[0199] The power output unit is used to iteratively perturb the final global optimal solution and accept the new solution using an annealing algorithm to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone.

[0200] In other embodiments of the present invention, the embodiments of the present invention provide the following technical solutions: a computer comprising a memory 102, a processor 101, and a computer program stored on the memory 102 and executable on the processor 101; the processor 101 implements the above-described artificial intelligence-based new energy vehicle charging management method when executing the computer program.

[0201] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or may be configured as one or more integrated circuits for implementing the embodiments of the present invention.

[0202] Memory 102 may include a large-capacity memory for data or instructions. By way of example, and not limitation, memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, memory 102 may include removable or non-removable (or fixed) media. Where appropriate, memory 102 may be internal or external to the data processing device. In certain embodiments, memory 102 is non-volatile memory. In certain embodiments, memory 102 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically alterable ROM (EAROM) or a flash memory (FLASH), or a combination of two or more of these. Under appropriate circumstances, the RAM can be a static random access memory (SRAM) or a dynamic random access memory (DRAM), where the DRAM can be a fast page mode dynamic random access memory (FPMDRAM), an extended data output dynamic random access memory (EDODRAM), a synchronous dynamic random access memory (SDRAM), etc.

[0203] The memory 102 may be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101 .

[0204] The processor 101 implements the above-mentioned artificial intelligence-based new energy vehicle charging management method by reading and executing computer program instructions stored in the memory 102.

[0205] In some embodiments, the computer may further include a communication interface 103 and a bus 100. Figure 3 As shown, the processor 101 , the memory 102 , and the communication interface 103 are connected via a bus 100 and communicate with each other.

[0206] The communication interface 103 is used to implement communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also implement data communication with other components such as external devices, image / data acquisition equipment, databases, external storage, and image / data processing workstations.

[0207] Bus 100 includes hardware, software, or both, and couples components of a computer device to each other. Bus 100 includes, but is not limited to, at least one of the following: a data bus, an address bus, a control bus, an expansion bus, and a local bus. By way of example and not limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Bus 100 may include one or more buses, where appropriate. Although embodiments of the present invention describe and illustrate a particular bus, the present invention contemplates any suitable bus or interconnect.

[0208] The computer can execute the artificial intelligence-based new energy vehicle charging management method of the present invention based on the acquisition of the artificial intelligence-based new energy vehicle charging management system, thereby realizing artificial intelligence-based new energy vehicle charging management.

[0209] In some further embodiments of the present invention, in combination with the above-mentioned artificial intelligence-based new energy vehicle charging management method, the embodiments of the present invention provide the following technical solutions: a storage medium having a computer program stored thereon, and the computer program, when executed by a processor, implements the above-mentioned artificial intelligence-based new energy vehicle charging management method.

[0210] Those skilled in the art will appreciate that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device), or in conjunction with such instruction execution system, apparatus, or device. For purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by an instruction execution system, apparatus, or device, or in conjunction with such instruction execution system, apparatus, or device.

[0211] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0212] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the aforementioned embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one or a combination of the following technologies known in the art may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0213] The technical features of the above-mentioned embodiments can be combined arbitrarily. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0214] The above-described embodiments merely illustrate several implementations of the present invention, and while their descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent. It should be noted that a person skilled in the art would be able to make numerous variations and improvements without departing from the spirit of the present invention, all of which fall within the scope of protection of the present invention. Therefore, the scope of protection of the patent for this invention shall be determined by the appended claims.

Claims

1. A new energy vehicle charging management method based on artificial intelligence, characterized in that: include: Obtain historical vehicle information and road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the road network information; Determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each time zone based on the time load diagram; Building a charging optimization management model based on the final charging price; An improved particle swarm optimization algorithm is used to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone; Charging the new energy vehicle to be charged based on the final charging power to implement a charging management process; The charging optimization management model is: ； Where, 、 Represent the first and second objective function values ​​respectively, Indicates the The load power corresponding to each time zone is: Indicates the average load power, Indicates the New energy vehicles waiting to be charged are Charging power in each time zone, Respectively represent the number of time zones and the number of new energy vehicles to be charged. Indicates the number of charging states, Indicates the The final charging price corresponding to the time zone, Indicates the length of a single time zone. Indicates the The battery life of a new energy vehicle to be charged, Indicates the The maximum total battery capacity of each new energy vehicle to be charged, Indicates the linear coefficient between battery life and charge and discharge cycle number, Indicates the New energy vehicles waiting to be charged are SOC of each time zone.

2. The artificial intelligence-based new energy vehicle charging management method according to claim 1, characterized in that: The step of determining the total vehicle charging load based on the historical vehicle information and the driving road network information includes: Extracting original traffic flow data and a set of factors influencing the traffic flow data from the historical vehicle information, and decomposing the original traffic flow data to obtain a plurality of decomposition components; Performing feature selection and feature integration on the vehicle flow data influencing factor set and the plurality of decomposition components to obtain a selected feature set; Obtaining training vehicle flow data, and inputting the training vehicle flow data into a preset vehicle flow prediction model for prediction to obtain predicted vehicle flow data; Determine the historical driving speed and driving power consumption of new energy vehicles based on the predicted traffic flow data; The vehicle SOC is determined based on the initial power and driving power consumption of historical new energy vehicles, and the vehicle probability is calculated based on the vehicle SOC. : ； Where, Indicates the The SOC of the last trip of a new energy vehicle in history, Indicates the The standard deviation of the SOC of historical new energy vehicles, Indicates the The expected vehicle SOC of a historical new energy vehicle; The historical new energy vehicles with a probability greater than the preset probability are regarded as the main new energy vehicles, and the path planning algorithm is used to determine the driving path of the main new energy vehicles to the target charging station; The path power consumption of the main new energy vehicles on the driving route is determined based on the driving speed of the main new energy vehicles, and the individual charging load of the main new energy vehicles is determined based on the path power consumption. The individual charging loads of all the main new energy vehicles in the same charging time period are superimposed in sequence to obtain the total vehicle charging load.

3. The artificial intelligence-based new energy vehicle charging management method according to claim 1, characterized in that: The step of determining the final charging price corresponding to each time zone based on the time load graph includes: Identify the maximum total vehicle charging load and the minimum total vehicle charging load in the time load diagram, and use the time zone corresponding to the maximum total vehicle charging load as the peak time zone and the time zone corresponding to the total vehicle charging load as the trough time zone. Total load with minimum car charging Calculate the first standard value With the second standard value : ； ； Where, Indicates the The total vehicle charging load corresponding to each time zone; Based on the first standard value With the second standard value Calculate similarity matrix : ； Where, Represents similarity matrix Middle Row, No. Elements of the row, is the adjustment coefficient, is the number of time zones; For the similarity matrix Perform several quadratic solutions until , if the matrix Elements in If it is greater than the classification threshold, the time zone and time zone Belonging to the same category, the time zone that belongs to the same category as the peak time zone is taken as the peak time zone, the time zone that belongs to the same category as the trough time zone is taken as the trough time zone, and the other time zones are taken as the flat time zones, so as to obtain a set of classified time zones; Performing fitting adjustment on the time load graph based on the set of classified time zones to obtain an adjusted time load graph; A final charging price corresponding to each of the time zones is determined based on the adjusted time load map.

4. The artificial intelligence-based new energy vehicle charging management method according to claim 3 is characterized in that: The step of determining the final charging price corresponding to each time zone based on the adjusted time load graph includes: Constructing a price objective function based on the adjustment time load diagram : ； ； ； ； ； Where, Represent the first, second, third, and fourth function values ​​respectively, 、 、 、 Respectively represent The electricity purchase cost price, charging price to be solved, lower limit of price fluctuation range, and upper limit of price fluctuation range corresponding to each time zone. 、 Represents the time load diagram, the adjusted time load diagram, and The load corresponding to each time zone, Indicates the time load diagram and the adjustment time load diagram The difference between the loads corresponding to the time zones, represents the price volatility factor, represents the standard deviation of the charging price to be solved, Indicates the The price discount coefficient of each new energy vehicle to be charged, Indicates the The ideal charging price for each new energy vehicle to be charged. Indicates the user's sensitivity to charging prices, 、 Respectively represent New energy vehicles waiting to be charged are Remaining power and ideal power in each time zone, Indicates the The response coefficient of the user to the charging power of each new energy vehicle to be charged, They represent the number of time zones and the number of new energy vehicles to be charged respectively; The objective function is solved by genetic algorithm The charging price to be solved in Solve to output the final charging price corresponding to each time zone.

5. The artificial intelligence-based new energy vehicle charging management method according to claim 1, characterized in that: The step of using the improved particle swarm optimization algorithm to solve the charging optimization management model to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone includes: Determine a number of particles according to the charging optimization management model, initialize the position and velocity of each particle, and calculate the fitness of each particle; Iteratively update the local optimal solution and the global optimal solution according to the fitness of the particles; Calculate and update inertia weight : ； Where, Represent the maximum and minimum set weights respectively, 、 、 Respectively represent the particle fitness of the current iteration number, the average particle fitness, and the minimum particle fitness, Respectively represent the current number of iterations and the maximum number of iterations, represents the decay constant; Update the inertia weight based on Determine the final charging power of each new energy vehicle to be charged in the corresponding time zone.

6. The artificial intelligence-based new energy vehicle charging management method according to claim 5, characterized in that: The updated inertia weight is based on The steps of determining the final charging power of each new energy vehicle to be charged in the corresponding time zone include: Update the inertia weight based on the , the local optimal solution of the current iteration number and the global optimal solution update the position and velocity of the particle: ； ； Where, 、 Respectively represent 、 The velocity of the particle after iterations, Respectively represent 、 The position of the particle after iterations, Represent the first and second learning rates respectively, Represent the first and second random numbers respectively, Respectively represent 、 Local optimal solution and global optimal solution after iterations; Iterate and repeat the process of updating the particle's position and velocity until the stopping condition is met, and output the final global optimal solution; The final global optimal solution is iteratively disturbed and the new solution is accepted using an annealing algorithm to obtain the final charging power of each new energy vehicle to be charged in the corresponding time zone.

7. A new energy vehicle charging management system based on artificial intelligence, the system adopts the new energy vehicle charging management method based on artificial intelligence as claimed in claim 1, characterized in that: The system comprises: A load module is used to obtain historical vehicle information and road network information of historical new energy vehicles, and determine the total vehicle charging load based on the historical vehicle information and the road network information; a pricing module, configured to determine a time load diagram based on the total vehicle charging load and a preset period, divide the preset period into a plurality of time zones, and determine a final charging price corresponding to each of the time zones based on the time load diagram; an optimization module, configured to construct a charging optimization management model based on the final charging price; A solution module, configured to solve the charging optimization management model using an improved particle swarm optimization algorithm to obtain a final charging power of each new energy vehicle to be charged in a corresponding time zone; A management module is used to charge the new energy vehicle to be charged based on the final charging power to implement a charging management process.

8. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the artificial intelligence-based new energy vehicle charging management method according to any one of claims 1 to 6 is implemented.

9. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the artificial intelligence-based new energy vehicle charging management method according to any one of claims 1 to 6.

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