A new energy vehicle charging management system and charging device

Abstract

The present invention relates to the technical field of charging management, and provides a new energy vehicle charging management system and a charging device, wherein a cloud server is connected to a user mobile terminal and a charging device for communication data, and comprises a battery analysis module and a charging planning module; the battery analysis module obtains battery performance data from the charging device, establishes a battery internal resistance prediction model, and when the charging device is connected to a new battery, calculates the charging time based on the battery internal resistance prediction model; the charging planning module is connected to the battery analysis module for data, obtains charging device related information from the charging device, and performs charging planning in combination with user vehicle related information obtained from the user mobile terminal; finally, the data is transmitted to the user terminal and pushed to the reservation user; the present invention solves the problem of inaccurate charging time estimation of the existing charging management system by constructing a battery internal resistance prediction model, allowing users to accurately judge the charging time and arrange travel plans, while improving the utilization rate of the charging device.

Description

Technical Field

[0001] The present invention relates to the technical field of charging management, and in particular to a new energy vehicle charging management system and a charging device. Background Art

[0002] With the improvement of environmental awareness and the strengthening of emission restrictions on traditional fuel vehicles, the sales of new energy vehicles continue to grow, which has led to a sharp increase in the demand for charging facilities. In order to meet the charging needs of new energy vehicles, the government has vigorously promoted the construction of charging infrastructure, and market capital has also actively invested. The number of charging devices has shown a rapid growth trend, and the distribution range has gradually expanded from cities to rural areas, highway service areas and other areas, and the coverage rate of charging devices across the country has continued to increase.

[0003] At the same time, with the application of technologies such as 5G and the Internet of Things, charging management systems and charging devices are constantly developing in the direction of intelligence and networking. Users can remotely control charging, monitor charging status and charging progress in real time, and obtain location information of charging devices through the mobile charging applet on their mobile phones, making charging services more convenient and efficient. Among them, charging time and SOC (state of charge) are one of the important information displayed in the mobile charging applet, which is mainly calculated based on the remaining battery power, charging device power and battery charging characteristics;

[0004] The existing mini-programs still have the following deficiencies in charging management:

[0005] 1. The existing charging management system does not accurately estimate the charging time, resulting in the actual charging time required being longer than the waiting time displayed by the mini program. The reason is that the actual charging time estimate of the battery will be affected by many factors, such as the degree of battery degradation, the charging environment, etc., which will cause different new energy vehicles to have inaccurate or changing charging times during the charging process, which in turn causes users to be unable to accurately judge the remaining charging time, affecting travel plans and reducing the user experience. Moreover, inaccurate charging time estimates will also lead to reduced utilization of the charging device, because other users may give up using the charging device because they see the wrong estimated time.

[0006] 2. The existing charging management system lacks the function of charging planning for users. For new energy vehicle users who drive long distances, they need to reasonably plan charging sites and charging time. However, many current charging applets simply provide the location and basic information of the charging device, without more detailed information such as the charging device occupancy time, etc., and because of the inaccurate charging time estimation, the actual charging time required by the user deviates greatly from the estimated time, affecting the user's travel plan. Summary of the invention

[0007] In order to solve the above technical problems, the present invention provides a new energy vehicle charging management system and a charging device. By establishing a battery internal resistance prediction model, when the charging device is connected to a new battery, the charging time is accurately calculated based on the battery internal resistance prediction model, and the charging plan is performed in combination with the user vehicle location information and vehicle speed information obtained from the user mobile terminal to solve the problems existing in the prior art.

[0008] A new energy vehicle charging management system, comprising a charging device, a cloud server and a user mobile terminal;

[0009] The cloud server is connected to the user's mobile terminal and the charging device for data communication, and includes a battery analysis module and a charging planning module; the battery analysis module obtains battery performance data from the charging device, establishes a battery internal resistance prediction model, and when the charging device is connected to a new battery, accurately calculates the charging time based on the battery internal resistance prediction model;

[0010] The charging planning module is data-connected with the battery analysis module, obtains the geographical location information of the charging device and the charging time of the charging device calculated by the battery analysis module from the charging device, and then performs charging planning in combination with the user vehicle location information and vehicle speed information obtained from the user mobile terminal;

[0011] The user mobile terminal includes a user management module, a charging reservation module, a charging control module, a fee settlement module, a push module and an information acquisition module;

[0012] The user management module provides a user operation interface for displaying the operation entrances of other modules in the user's mobile terminal, allowing the user to view and operate other modules in the user's mobile terminal through the user management module;

[0013] The charging reservation module makes recommendations to users based on the charging planning module. The recommendations include charging planning recommendations, the nearest available charging location, and the nearest charging location with the shortest remaining charging time. It displays vehicle speed, vehicle location, charging device location information, charging device availability, and the remaining charging time of each charging device in use in real time.

[0014] The charging control module displays the remaining charging time to the user based on the battery analysis module, and monitors the charging data related to the charging device, including voltage, current, power and SOC, and can automatically cut off the power when abnormal charging data is monitored;

[0015] The fee settlement module settles the fees based on the current charging fee of the charging device and provides a payment window to be displayed in the user operation interface provided by the user management module;

[0016] The information acquisition module acquires the user's vehicle information based on the user's mobile terminal, and the information includes the vehicle location, vehicle speed and vehicle remaining mileage.

[0017] Preferably, the battery analysis module includes an internal resistance prediction model unit and a charging time calculation unit;

[0018] The internal resistance prediction model unit obtains relevant battery data from relevant new energy vehicle battery manufacturers to construct an internal resistance prediction model;

[0019] The relevant battery data includes battery basic data, battery initial charging data and real-time charging data;

[0020] The basic battery data includes the battery model and nominal capacity;

[0021] The battery initial charging data includes the voltage at the moment of charging insertion, i.e., the current at the beginning of charging, the remaining battery power at the beginning of charging, and the battery temperature data at the beginning of charging;

[0022] The real-time charging data includes the voltage, current, temperature data during the charging process acquired at a certain time interval Δt during the charging process, and the real-time internal resistance data calculated by the battery electromotive force or the battery open circuit voltage;

[0023] The charging time calculation unit is used to obtain the initial charging data and basic battery data of the rechargeable battery when the charging device is connected to the new energy vehicle for charging, input the initial charging information into the internal resistance prediction model, output the internal resistance change curve of the rechargeable battery during charging, and calculate the charging time by combining the internal resistance change curve with the power of the charging device.

[0024] Preferably, the specific construction process of the internal resistance prediction model is as follows:

[0025] Let t represent time, and the time taken for the battery charging process is the set (t0, t1, t2, ..., t 满电 ), the battery capacity is expressed as C, that is, the nominal capacity of the battery is C 总 , the battery remaining capacity at the beginning of charging is The time when charging starts is t0, and the voltage when charging starts is The current at the beginning of charging is The remaining battery capacity at the start of charging is The internal resistance at the beginning of charging is The battery temperature is

[0026] Select the data at the beginning of charging as the input feature vector

[0027] The radial basis kernel function K is used to calculate the battery internal resistance change problem;

[0028]

[0029] Among them, a and b are the indexes of vector features, exp represents the exponential function with the base being the natural constant e, and γ is a hyperparameter that determines the width of the radial basis function. When its value is larger, the value of the kernel function decreases faster as the distance between data points increases. It measures the similarity between input feature vectors and maps the original input space to a high-dimensional space, making it easier to find linear or simpler nonlinear relationships in the high-dimensional space to describe the data. In the battery internal resistance prediction model, the choice of γ will affect the model's judgment on the similarity of initial data of different batteries, and thus affect the model's prediction performance; the accurate value is determined by cross-validation, and the kernel function K is the basis for constructing a decision function to calculate the change in internal resistance;

[0030] The goal of the model is to find a decision function that minimizes the difference between the predicted value and the true value, for the internal resistance prediction model;

[0031] Let the input feature vector Dataset

[0032] Where n is the total number of input feature vectors, R a is the corresponding input feature vector The actual internal resistance value when

[0033] Assume the decision function is:

[0034]

[0035] in, is the predicted value of internal resistance calculated by the model, w is the weight, and g is the bias term;

[0036] Construct a weight-constrained optimization objective:

[0037]

[0038] The constraints are:

[0039]

[0040] Introducing the Lagrange multiplier α a To handle inequality constraints Construct the Lagrangian function L;

[0041]

[0042] Find the partial derivatives of w and g respectively;

[0043]

[0044] so Substitute w into L to obtain the Lagrangian function L of the dual problem. Through the duality of Lagrangian, the weight optimization objective of the original problem is converted into a dual problem and solved. Get the Lagrange multiplier α a The value of

[0045] Finally, w and b are substituted into the preset decision function and replaced using the properties of the kernel function;

[0046] The final decision function form is:

[0047]

[0048] in It is the feature vector output during actual calculation;

[0049] Evaluate and optimize the model;

[0050] Evaluation indicators: The mean square error is used to evaluate the performance of the model. The formula is:

[0051]

[0052] The smaller the mean square error, the better the model performance;

[0053] Parameter adjustment: The model parameters are adjusted through cross-validation, including the bias term and the hyperparameters of the kernel function. During the adjustment process, the trained model is verified using real-time data. During the charging process, the real-time collected internal resistance data is compared with the internal resistance predicted by the model. Based on the comparison results, the model parameters are further adjusted to make the model calculation results correspond to the actual situation.

[0054] Preferably, the specific calculation process of the charging time calculation unit is as follows:

[0055] First, calculate the time set (t0, t1, t2, ..., t 满电 ) corresponds to the predicted value of internal resistance at each moment Get a function of the relationship between internal resistance and time

[0056] The remaining battery charging time calculation process is as follows:

[0057] Assume the amount of electricity to be charged is Charging power is P = UI, the internal resistance changes with time

[0058] According to the function of internal resistance changing with time Calculate the charging efficiency as a function of time η(t):

[0059]

[0060] Calculation of remaining charging time, for the set (t0, t1, t2, ..., t 满电 )The interval between each t is Δt;

[0061] The initial charging time is recorded as t0. For the time from the initial charging time t0 to the final charging completion time t 满电 The entire charging process:

[0062] Assume that the time interval Δt between each t is small enough to ensure that the charging efficiency remains unchanged within each Δt. Assume that the remaining charging time is t 剩余 , the total charging time is t 剩余 +t0 is calculated by integration:

[0063] First, calculate the amount of charge ΔC charged within the time interval Δt:

[0064] ΔC=P×η(t)Δt

[0065] When the accumulated charge reaches C 总 When charging is completed, that is:

[0066]

[0067] Solving for t 剩余 , then it is expressed as:

[0068]

[0069] Among them, d is the differential symbol in differential calculation, Substituting in:

[0070]

[0071] Among them, the remaining charging time is calculated as t 剩余 .

[0072] Preferably, the charging planning module includes a vehicle charging information unit and a vehicle charging configuration unit;

[0073] The vehicle charging information unit obtains the number of charging devices in a charging station, and selects the location information and speed information of a specified number of user new energy vehicles that use the user mobile terminal to make a charging reservation closest to the charging station. The specified number is specified as follows: the specified number selected near each charging station is equal. If they cannot be equal, the vehicle charging information unit selects based on the principle of proximity;

[0074] The vehicle charging configuration unit performs charging planning based on the location information and speed information of a specified number of new energy vehicles acquired by the vehicle charging information unit and the remaining charging time of the charging devices in use at the charging station calculated by the charging time calculation unit.

[0075] Preferably, the vehicle charging configuration unit first calculates the time t taken to reach the charging station based on the location information and speed information of the user's new energy vehicle for charging reservation. 车 ;

[0076] Assume that the number of charging devices is m. For the vth charging device, v = 1, 2, ..., m, the remaining charging time is Suppose there are y charging reservation vehicles. For the jth reservation user vehicle, j = 1, 2, ..., y, the time required to reach the charging station is

[0077] Based on the above data, charging planning is carried out with the following constraints:

[0078] For each charging device v, only one vehicle can be charged at a time:

[0079] The mathematical formula is:

[0080]

[0081] Among them, H LO is a decision variable. When vehicle j is assigned to charging device v, H LO =1, otherwise H LO =0;

[0082] Each vehicle can only be assigned to one charging device:

[0083] The mathematical formula is:

[0084]

[0085] For any vehicle j, ensure that it is assigned to and only assigned to one charging device;

[0086] In order to minimize the total waiting time of all vehicles, an objective function is defined to measure it; let the waiting time of any vehicle j be If vehicle j is assigned to charging device v, then:

[0087]

[0088] The total waiting time is the sum of the waiting times of all vehicles, and the objective function is:

[0089]

[0090] After the objective function is determined, an integer programming solver is used to solve the combination of vehicles assigned to charging piles;

[0091] The objective function and constraints are input into the solver, and the solver will find the optimal solution that satisfies all constraints and minimizes the objective function. This solution is the charging plan output by the car charger configuration unit. Many mathematical software and tools provide integer programming solvers, including CPLEX and Gurobi.

[0092] Preferably, the user mobile terminal can be installed in new energy vehicle computers and mobile phones, and can be used in mobile phones and vehicle computers in the form of small programs. The vehicle speed, remaining power and vehicle location are collected through the charging planning module. When the user turns on the push function, it is used to automatically perform charging planning and trigger push when the battery is low.

[0093] A new energy vehicle charging device, comprising a charging pile;

[0094] A power input module, which is installed on the charging pile and is used to receive external power, including but not limited to a power interface, a filter and a protection circuit;

[0095] A power conversion and output module, which is electrically connected to the power input module and installed on the charging pile, including but not limited to a transformer, a rectifier, and an inverter assembly, and is used to convert the voltage and current of the power input into a voltage and current suitable for the charged device to charge the battery. The conversion method includes using a high-frequency switching power supply technology for a DC charging device to convert AC power into DC power, and adjusting the output voltage and current to meet the charging requirements of different devices;

[0096] A battery data acquisition module is installed on the charging pile to obtain the basic data of the vehicle battery of the device and the remaining battery power information at the beginning of charging through the BMS system on the new energy vehicle;

[0097] The basic battery data includes the battery model, nominal capacity, and initial internal resistance;

[0098] The battery is charged and inserted at the moment of charging by using a current sensor, a voltage sensor and a temperature sensor to collect the voltage at the beginning of charging, the current at the beginning of charging, the battery temperature and the ambient temperature;

[0099] A display module, which is data-connected to the battery data acquisition module and installed on the charging pile, includes a display screen and an indicator light. The display screen displays the charging progress, battery level and charging time. The indicator light can use different colors to indicate different charging states. A constant red light indicates charging, a constant green light indicates full charge, and a flashing yellow light indicates a fault.

[0100] The communication module is connected to the battery data acquisition module and installed on the charging pile to transmit the data collected by the battery data acquisition module to the cloud server through remote communication.

[0101] Compared with the prior art, the present invention has the following beneficial effects:

[0102] 1. The present invention constructs a battery internal resistance prediction model and combines the power of the charging device to accurately calculate the charging time, thereby solving the problem of inaccurate or changing charging time estimation in the existing charging management system, allowing users to accurately judge the remaining charging time, reasonably arrange travel plans, and improve the user experience, while also improving the utilization rate of the charging device.

[0103] 2. The present invention performs charging planning by combining the user's vehicle location information, speed information, charging device geographic location information and usage information through a charging planning module, and recommends charging plans for users, thereby solving the problem that existing charging management systems lack charging planning functions for users, and achieving the purpose of providing personalized charging strategy planning for new energy vehicle users who drive long distances, and meeting the needs of users to reasonably plan charging sites and charging times based on factors such as the vehicle's remaining power, cruising range, and the distance between charging stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 It is a schematic diagram of the working process of the charging device of the present invention;

[0105] Figure 2 This is a schematic diagram of the workflow when the user uses the present invention;

[0106] Figure 3 It is a schematic diagram of the working process of each module of the present invention;

[0107] Figure 4 It is a schematic diagram of the working process of the battery analysis module of the present invention;

[0108] Figure 5 It is a schematic diagram of the working process of the charging planning module of the present invention. DETAILED DESCRIPTION

[0109] The following embodiments of the present invention are described in further detail in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0110] The present invention provides a new energy vehicle charging management system, including a charging device, a cloud server and a user mobile terminal;

[0111] The cloud server is connected to the user's mobile terminal and the charging device for data communication, and includes a battery analysis module and a charging planning module; the battery analysis module obtains battery performance data from the charging device, establishes a battery internal resistance prediction model, and when the charging device is connected to a new battery, accurately calculates the charging time based on the battery internal resistance prediction model;

[0112] The charging planning module is data-connected with the battery analysis module, obtains the geographical location information of the charging device and the charging time of the charging device calculated by the battery analysis module from the charging device, and then performs charging planning in combination with the user vehicle location information and vehicle speed information obtained from the user mobile terminal;

[0113] The user mobile terminal includes a user management module, a charging reservation module, a charging control module, a fee settlement module, a push module and an information acquisition module;

[0114] The user management module provides a user operation interface for displaying the operation entrances of other modules in the user's mobile terminal, allowing the user to view and operate other modules in the user's mobile terminal through the user management module;

[0115] The charging reservation module makes recommendations to users based on the charging planning module. The recommendations include charging planning recommendations, the nearest available charging location, and the nearest charging location with the shortest remaining charging time. It displays vehicle speed, vehicle location, charging device location information, charging device availability, and the remaining charging time of each charging device in use in real time.

[0116] The charging control module displays the remaining charging time to the user based on the battery analysis module, and monitors the charging data related to the charging device, including voltage, current, power and SOC, and can automatically cut off the power when abnormal charging data is monitored;

[0117] The fee settlement module settles the fees based on the current charging fee of the charging device and provides a payment window to be displayed in the user operation interface provided by the user management module;

[0118] The information acquisition module acquires the user's vehicle information based on the user's mobile terminal, and the information includes the vehicle location, vehicle speed and vehicle remaining mileage.

[0119] A new energy vehicle charging device, comprising a charging pile;

[0120] A power input module, which is installed on the charging pile and is used to receive external power, including but not limited to a power interface, a filter and a protection circuit;

[0121] A power conversion and output module, which is electrically connected to the power input module and installed on the charging pile, including but not limited to a transformer, a rectifier, and an inverter assembly, and is used to convert the voltage and current of the power input into a voltage and current suitable for the charged device to charge the battery. The conversion method includes using a high-frequency switching power supply technology for a DC charging device to convert AC power into DC power, and adjusting the output voltage and current to meet the charging requirements of different devices;

[0122] A battery data acquisition module is installed on the charging pile to obtain the basic data of the vehicle battery of the device and the remaining battery power information at the beginning of charging through the BMS system on the new energy vehicle;

[0123] The basic battery data includes the battery model, nominal capacity, and initial internal resistance;

[0124] The battery is charged and inserted at the moment of charging by using a current sensor, a voltage sensor and a temperature sensor to collect the voltage at the beginning of charging, the current at the beginning of charging, the battery temperature and the ambient temperature;

[0125] A display module, which is data-connected to the battery data acquisition module and installed on the charging pile, includes a display screen and an indicator light. The display screen displays the charging progress, battery level and charging time. The indicator light can use different colors to indicate different charging states. A constant red light indicates charging, a constant green light indicates full charge, and a flashing yellow light indicates a fault.

[0126] The communication module is connected to the battery data acquisition module and installed on the charging pile to transmit the data collected by the battery data acquisition module to the cloud server through remote communication.

[0127] Embodiment 1:

[0128] The cloud server obtains relevant battery data for training and establishing the internal resistance prediction model from the relevant new energy vehicle system in advance, and the battery analysis module performs analysis through the internal resistance prediction model unit;

[0129] The relevant battery data obtained include basic battery data, battery model and nominal capacity;

[0130] Initial battery charging data: voltage at the start of charging, current at the start of charging, remaining battery capacity at the start of charging, and battery temperature at different remaining capacities;

[0131] Real-time charging data: During the charging process, the voltage, current, and temperature are collected at a specified time interval Δt. The real-time internal resistance is calculated by the battery electromotive force or the battery open circuit voltage. The specified time interval Δt ensures that the internal resistance does not change significantly. For example, Δt can be set to one minute.

[0132] According to the corresponding new energy vehicle models, build an internal resistance prediction model for each type of vehicle during charging:

[0133] Let t represent time, and the time taken for the battery charging process is the set (t0, t1, t2, ..., t 满电 ), the battery capacity is expressed as C, that is, the nominal capacity of the battery is C 总 , the battery remaining capacity at the beginning of charging is The time when charging starts is t0, and the voltage when charging starts is Current at the start of charging Remaining battery capacity when charging starts The internal resistance at the beginning of charging is Battery temperature

[0134] Select the data at the beginning of charging as the input feature vector

[0135] The radial basis kernel function K is used to calculate the battery internal resistance change problem;

[0136]

[0137] Among them, a, b are the indexes of vector features, exp represents the exponential function with the base being the natural constant e, and γ is a hyperparameter that determines the width of the radial basis function. When its value is larger, the value of the kernel function decreases faster as the distance between data points increases. It measures the similarity between input feature vectors and maps the original input space to a high-dimensional space, making it easier to find linear or simpler nonlinear relationships in the high-dimensional space to describe the data. In the battery internal resistance prediction model, the choice of γ will affect the model's judgment on the similarity of initial data of different batteries, and thus affect the model's prediction performance; the accurate value of γ is determined by cross-validation, and the kernel function K is the basis for constructing a decision function to determine the change in calculated internal resistance, because it affects the data relationship and space mapping through hyperparameters, and thus affects the model's judgment and prediction performance on battery data, and the hyperparameters are determined by cross-validation;

[0138] The goal of the model is to find a decision function that minimizes the difference between the predicted value and the true value. For the internal resistance prediction model, the data at the beginning of n charging are input into the feature vector as well as The corresponding actual internal resistance value R aCollections as Datasets

[0139] Where n is the total number of input feature vectors, R a It corresponds to The actual internal resistance value;

[0140] Assume the decision function is:

[0141]

[0142] in, is the predicted value of internal resistance calculated by the model, w is the weight, and g is the bias term;

[0143] Construct a weight-constrained optimization objective:

[0144]

[0145] The constraints are:

[0146]

[0147] Introducing the Lagrange multiplier α a To handle inequality constraints Construct the Lagrangian function L;

[0148]

[0149] Find the partial derivatives of w and g respectively;

[0150]

[0151] so Substitute w into L to obtain the Lagrangian function L of the dual problem. Through the duality of Lagrangian, the weight optimization objective of the original problem is converted into a dual problem and solved. Get the Lagrange multiplier α a The value of

[0152] Finally, we substitute w and b and use the properties of the kernel function to replace them. The final decision function form is:

[0153] in It is the feature vector output during actual calculation to evaluate and optimize the model;

[0154] Evaluation indicators: The mean square error is used to evaluate the performance of the model. The formula is:

[0155]

[0156] The smaller the mean square error, the better the model performance;

[0157] Parameter adjustment: The model parameters are adjusted through cross-validation, including the bias term and the hyperparameters of the kernel function. During the adjustment process, the trained model is verified using real-time data. During the charging process, the real-time collected data is compared with the internal resistance change predicted by the model. Based on the comparison results, the model parameters are further adjusted to make the model calculation results correspond to the actual situation.

[0158] Based on the internal resistance prediction model established for each vehicle's battery, by combining the initial charging information and the real-time charging information, an internal resistance prediction model is established to predict the internal resistance of the battery at each time point during charging. This achieves accurate prediction of the internal resistance changes caused by a series of factors such as different degrees of battery aging, different manufacturers, and different temperatures, thereby providing sufficient data basis for the subsequent accurate prediction of charging power and accurate calculation of charging time.

[0159] Embodiment 2:

[0160] like Figure 1-5 As shown, in this embodiment, the new energy vehicle owner Ding comes to a charging station using the charging device and charges the vehicle through the charging device.

[0161] During charging, the charging device instantly obtains the initial charging data of the new energy vehicle owner Ding's car and uploads it to the cloud server at the same time. The charging time calculation unit in the cloud server inputs the initial charging information of the car into the internal resistance prediction model, and outputs the internal resistance change curve of the rechargeable battery during charging. The internal resistance change curve is combined with the power of the charging device to calculate the accurate charging time. The calculation process is as follows;

[0162] First, calculate the set (t0, t1, t2, ..., t 满电 ) corresponds to the predicted value of internal resistance for each t Get a function of the relationship between internal resistance and time

[0163] The remaining battery charging time calculation process is as follows:

[0164] Assume the amount of electricity to be charged is Charging power is P = UI, the internal resistance changes with time

[0165] According to the function of internal resistance changing with time Calculate the charging efficiency as a function of time η(t):

[0166]

[0167] Calculation of remaining charging time, for the set (t0, t1, t2, ..., t 满电) The interval between each t is Δt, from the initial time t0 to the final charging completion time t 满电 The entire charging process;

[0168] Assume that the time interval Δt is small enough so that the charging efficiency remains constant within each Δt. Assume that the remaining charging time is t 剩余 , has been charged for time t0, the total charging time is t 剩余 +t0 is calculated by integration:

[0169] First, calculate the amount of electricity charged in the time interval Δt:

[0170] ΔC=P×η(t)Δt

[0171] When the accumulated charge reaches C 总 When charging is completed, that is:

[0172]

[0173] Solving for t 剩余 , then it is expressed as:

[0174]

[0175] Among them, d is the differential symbol in differential calculation, Substituting in:

[0176]

[0177] Among them, the remaining charging time is calculated as t 剩余 .

[0178] Through the first embodiment, a battery internal resistance prediction model has been constructed, and the initial charging information is obtained in combination with the charging device to accurately calculate the charging time, thereby solving the problem of inaccurate or changing charging time estimation of the existing charging management system, thereby achieving the purpose of allowing users to accurately judge the remaining charging time and improve the user experience.

[0179] During charging, the battery data collection module of the charging device collects the battery data related to the new energy vehicle being charged and uploads it to the cloud server;

[0180] The charging time calculation unit of the cloud server inputs the initial charging information into the internal resistance prediction model, outputs the internal resistance change curve of the rechargeable battery during charging, and calculates the charging time by combining the internal resistance change curve with the power of the charging device;

[0181] The charging control module displays the remaining charging time for the car owner based on the battery analysis module, and monitors the charging data related to the charging device, including voltage, current, power and SOC. It can remotely cut off the power when abnormal charging data is detected. The real-time monitoring function ensures the safety and reliability of the charging process. Once an abnormal situation occurs, the power can be remotely cut off in time to avoid potential safety risks and provide users with a safe charging environment.

[0182] Embodiment three;

[0183] like Figure 1-5 As shown, in this embodiment, the system is installed on the new energy vehicle computer and mobile phone of the new energy vehicle owner Ding, and the push function is turned on the computer and mobile phone. At the same time, the new energy vehicle owner Ding is preparing for a long-distance self-driving tour. He drives the new energy vehicle to City B and passes through City A.

[0184] Before departure, the car owner Ding opened the system through the car computer to make an appointment for charging and turned on the push function. The car owner first set the charging threshold in the system. As the vehicle continues to travel, the power will gradually decrease. The system plans the charging in advance according to the set threshold. For example, the distance to City A is closer and City B is farther away. According to the current power and the charging status of City A and City B, the information acquisition module continuously sends information such as the vehicle location, vehicle speed and remaining mileage to the cloud server, and the charging planning module begins to obtain information such as vehicle speed, remaining power and vehicle location for charging planning.

[0185] While the car is driving, at the charging station in City A, which is closest to the car owner D, the battery data collection module of the charging device collects the battery data of the new energy vehicle being charged and uploads it to the cloud server;

[0186] The charging time calculation unit of the cloud server inputs the initial charging information of the car being charged at the charging station in City A into the internal resistance prediction model, and outputs the internal resistance change curve of the rechargeable battery during charging. The charging time is calculated by combining the internal resistance change curve with the power of the charging device. The specific calculation process is as follows:

[0187] After calculating the remaining charging time of all charging devices, the charging reservation module will display this information to the car owner synchronously, and the display module on the charging device can also display relevant information;

[0188] Based on the establishment of a single internal resistance prediction model for each vehicle battery, the internal resistance change caused by a series of factors such as different aging, different manufacturers, and different temperatures can be accurately predicted, and the charging power calculation can be accurately predicted. The charging time can be accurately calculated, and users can be provided with real-time usage information of each charging station, including whether there are vacancies, waiting time, etc. This accuracy gives users more basis for planning charging trips during long-distance travel.

[0189] When faced with multiple charging station options, such as charging stations in City A and City B at different distances, users can avoid choosing a station that is fully charged or has a long waiting time due to insufficient information. By comprehensively considering the charging conditions of each station, users can plan in advance to charge at a more suitable station, optimize the entire itinerary, and avoid trip delays caused by charging problems, thereby greatly improving charging efficiency and overall user experience, allowing users to have more control over charging arrangements during their trip.

[0190] The vehicle charging information unit obtains the number of charging devices at the charging station in City A, selects the information of the specified number of new energy vehicles of users who use the user's mobile terminal to make a charging reservation closest to the charging station, including the location information and speed information. The specified number selected near each charging station is equal. If they cannot be equal, the vehicle charging information unit selects based on the principle of proximity; and the owner of the new energy vehicle D who has made a reservation for the charging plan is also included in the calculation range;

[0191] The vehicle charging configuration unit obtains the location information and speed information of the new energy vehicle of the owner Ding according to the vehicle charging information unit, and performs charging planning in combination with the remaining charging time of the charging device in use calculated by the battery analysis module. The vehicle charging configuration unit first calculates the time t taken to reach the charging station according to the location information and speed information of the new energy vehicle of the owner Ding. 车 ;

[0192] Assume that the number of charging devices is m. For the vth charging device, v = 1, 2, ..., m, the remaining charging time is Suppose there are y charging reservation vehicles. For the jth reservation user vehicle, j = 1, 2, ..., y, the time required to reach the charging station is

[0193] Based on the above data, charging planning is carried out with the following constraints:

[0194] For each charging device v, only one vehicle can be charged at a time:

[0195] The mathematical formula is:

[0196]

[0197] Among them, H LOis a decision variable. When vehicle j is assigned to charging device v, H LO =1, otherwise H LO =0;

[0198] Each vehicle can only be assigned to one charging device:

[0199] The mathematical formula is:

[0200]

[0201] For any vehicle j, ensure that it is assigned to and only assigned to one charging device;

[0202] In order to minimize the total waiting time of all vehicles, an objective function is defined to measure it; let the waiting time of each vehicle j be If vehicle j is assigned to charging device v, then:

[0203]

[0204] The total waiting time is the sum of the waiting times of all vehicles, and the objective function is:

[0205]

[0206] After the objective function is determined, an integer programming solver is used to solve the combination of vehicles assigned to charging piles. Many mathematical software and tools provide integer programming solvers including CPLEX and Gurobi.

[0207] The objective function and constraints are input into the solver, and the solver will find the optimal solution that satisfies all constraints and minimizes the objective function. This solution is the charging plan output by the vehicle charging configuration unit.

[0208] The charging planning module combines the user's vehicle location information, speed information, charging device geographic location information and usage information to plan charging, and recommend charging plans for users, solving the problem that the existing charging management system lacks charging planning functions for users, and achieves the purpose of providing personalized charging strategy planning for new energy vehicle users who drive long distances, and meets the user's needs to reasonably plan charging sites and charging times based on factors such as the vehicle's remaining power, cruising range, and the distance between charging stations.

[0209] The embodiments of the present invention are provided for the purpose of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations of the present invention. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A new energy vehicle charging management system, characterized in that: Including cloud servers and user mobile terminals; The cloud server is connected to the user's mobile terminal and the charging device for data communication, and includes a battery analysis module and a charging planning module; the battery analysis module obtains battery performance data from the charging device, establishes a battery internal resistance prediction model, and when the charging device is connected to a new battery, accurately calculates the charging time based on the battery internal resistance prediction model; The charging planning module is data-connected with the battery analysis module, obtains the geographical location information of the charging device and the charging time of the charging device calculated by the battery analysis module from the charging device, and then performs charging planning in combination with the user vehicle location information and vehicle speed information obtained from the user mobile terminal; The user mobile terminal includes a user management module, a charging reservation module, a charging control module, a fee settlement module, a push module and an information acquisition module; The user management module provides a user operation interface for displaying the operation entrances of other modules in the user's mobile terminal, allowing the user to view and operate other modules in the user's mobile terminal through the user management module; The charging reservation module makes recommendations to users based on the charging planning module. The recommendations include charging planning recommendations, the nearest available charging location, and the nearest charging location with the shortest remaining charging time. It displays vehicle speed, vehicle location, charging device location information, charging device availability, and the remaining charging time of each charging device in use in real time. The charging control module displays the remaining charging time to the user based on the battery analysis module, and monitors the charging data related to the charging device, including voltage, current, power and SOC, and can automatically cut off the power when abnormal charging data is monitored; The fee settlement module settles the fees based on the current charging fee of the charging device and provides a payment window to be displayed in the user operation interface provided by the user management module; The information acquisition module acquires the user's vehicle information based on the user's mobile terminal, and the information includes the vehicle location, vehicle speed and vehicle remaining mileage.

2. A new energy vehicle charging management system as claimed in claim 1, characterized in that: The battery analysis module includes an internal resistance prediction model unit and a charging time calculation unit; The internal resistance prediction model unit obtains relevant battery data from relevant new energy vehicle battery manufacturers to construct an internal resistance prediction model; The relevant battery data includes battery basic data, battery initial charging data and real-time charging data; The basic battery data includes the battery model and nominal capacity; The battery initial charging data includes the voltage at the moment of charging insertion, i.e., the current at the beginning of charging, the remaining battery power at the beginning of charging, and the battery temperature data at the beginning of charging; The real-time charging data includes the voltage, current, temperature data during the charging process acquired at a certain time interval Δt during the charging process, and the real-time internal resistance data calculated by the battery electromotive force or the battery open circuit voltage; The charging time calculation unit is used to obtain the initial charging data and basic battery data of the rechargeable battery when the charging device is connected to the new energy vehicle for charging, input the initial charging information into the internal resistance prediction model, output the internal resistance change curve of the rechargeable battery during charging, and calculate the charging time by combining the internal resistance change curve with the power of the charging device.

3. A new energy vehicle charging management system as claimed in claim 2, characterized in that: The specific construction process of the internal resistance prediction model is as follows: Let t represent time, and the time taken for the battery charging process is the set (t0, t1, t2, ..., t 满电 ), the battery capacity is expressed as C, that is, the nominal capacity of the battery is C 总 , the battery remaining capacity at the beginning of charging is The time when charging starts is t0, and the voltage when charging starts is The current at the beginning of charging is The remaining battery capacity at the start of charging is The internal resistance at the beginning of charging is The battery temperature is Select the data at the beginning of charging as the input feature vector The radial basis kernel function K is used to calculate the battery internal resistance change problem; Among them, a and b are the indexes of vector features, exp represents the exponential function with the base being the natural constant e, and γ is a hyperparameter that determines the width of the radial basis function. When its value is larger, the value of the kernel function decreases faster as the distance between data points increases. It measures the similarity between input feature vectors and maps the original input space to a high-dimensional space, making it easier to find linear or simpler nonlinear relationships in the high-dimensional space to describe the data. In the battery internal resistance prediction model, the choice of γ will affect the model's judgment on the similarity of initial data of different batteries, and thus affect the model's prediction performance; the accurate value is determined by cross-validation, and the kernel function K is the basis for constructing a decision function to calculate the change in internal resistance; The goal of the model is to find a decision function that minimizes the difference between the predicted value and the true value, for the internal resistance prediction model; Let the input feature vector Dataset Where n is the total number of input feature vectors, R a is the corresponding input feature vector The actual internal resistance value when Assume the decision function is: in, is the predicted value of internal resistance calculated by the model, w is the weight, and g is the bias term; Construct a weight-constrained optimization objective: The constraints are: Introducing the Lagrange multiplier α a To handle inequality constraints Construct the Lagrangian function L; Find the partial derivatives of w and g respectively; so Substitute w into L to obtain the Lagrangian function L of the dual problem. Through the duality of Lagrangian, the weight optimization objective of the original problem is converted into a dual problem and solved. Get the Lagrange multiplier α a The value of Finally, w and b are substituted into the preset decision function and replaced using the properties of the kernel function; The final decision function form is: in It is the feature vector output during actual calculation; Evaluate and optimize the model; Evaluation indicators: The mean square error is used to evaluate the performance of the model. The formula is: The smaller the mean square error, the better the model performance; Parameter adjustment: The model parameters are adjusted through cross-validation, including the bias term and the hyperparameters of the kernel function. During the adjustment process, the trained model is verified using real-time data. During the charging process, the real-time collected internal resistance data is compared with the internal resistance predicted by the model. Based on the comparison results, the model parameters are further adjusted to make the model calculation results correspond to the actual situation.

4. A new energy vehicle charging management system as claimed in claim 3, characterized in that: The specific calculation process of the charging time calculation unit is as follows: First, calculate the time set (t0, t1, t2, ..., t 满电 ) corresponds to the predicted value of internal resistance at each moment Get a function of the relationship between internal resistance and time The remaining battery charging time calculation process is as follows: Assume the amount of electricity to be charged is Charging power is P = UI, the internal resistance changes with time According to the function of internal resistance changing with time Calculate the charging efficiency as a function of time η(t): Calculation of remaining charging time, for the set (t0, t1, t2, ..., t 满电 )The interval between each t is Δt; The initial charging time is recorded as t0. For the time from the initial charging time t0 to the final charging completion time t 满电 The entire charging process: Assume that the time interval Δt between each t is small enough to ensure that the charging efficiency remains unchanged within each Δt. Assume that the remaining charging time is t 剩余 , the total charging time is t 剩余 +t0 is calculated by integration: First, calculate the amount of charge ΔC charged within the time interval Δt: ΔC=P×η(t)Δt When the accumulated charge reaches C 总 When charging is completed, that is: Solving for t 剩余 , then it is expressed as: Among them, d is the differential symbol in differential calculation, Substituting in: Among them, the remaining charging time is calculated as t 剩余 .

5. A new energy vehicle charging management system as claimed in claim 4, characterized in that: The charging planning module includes a vehicle charging information unit and a vehicle charging configuration unit; The vehicle charging information unit obtains the number of charging devices in a charging station, and selects the location information and speed information of a specified number of user new energy vehicles that use the user mobile terminal to make a charging reservation closest to the charging station. The specified number is specified as follows: the specified number selected near each charging station is equal. If they cannot be equal, the vehicle charging information unit selects based on the principle of proximity; The vehicle charging configuration unit performs charging planning based on the location information and speed information of a specified number of new energy vehicles acquired by the vehicle charging information unit and the remaining charging time of the charging devices in use at the charging station calculated by the charging time calculation unit.

6. A new energy vehicle charging management system as claimed in claim 5, characterized in that: The vehicle charging configuration unit first calculates the time t taken to reach the charging station based on the location information and speed information of the user's new energy vehicle for charging reservation. 车 ; Assume that the number of charging devices is m. For the vth charging device, v = 1, 2, ..., m, the remaining charging time is Suppose there are y charging reservation vehicles. For the jth reservation user vehicle, j = 1, 2, ..., y, the time required for it to reach the charging station is t O 车 ; Based on the above data, charging planning is carried out with the following constraints: For each charging device v, only one vehicle can be charged at a time: The mathematical formula is: Among them, H LO is a decision variable. When vehicle j is assigned to charging device v, H LO =1, otherwise H LO =0; Each vehicle can only be assigned to one charging device: The mathematical formula is: For any vehicle j, ensure that it is assigned to and only assigned to one charging device; In order to minimize the total waiting time of all vehicles, an objective function is defined to measure it; let the waiting time of any vehicle j be If vehicle j is assigned to charging device v, then: The total waiting time is the sum of the waiting times of all vehicles, and the objective function is: After the objective function is determined, an integer programming solver is used to solve the combination of vehicles assigned to charging piles; The objective function and constraints are input into the solver, and the solver will find the optimal solution that satisfies all constraints and minimizes the objective function. This solution is the charging plan output by the vehicle charging configuration unit.

7. A new energy vehicle charging management system as claimed in claim 1, characterized in that: The user mobile terminal can be installed in new energy vehicle computers and mobile phones, and can be used in mobile phones and vehicle computers in the form of small programs. The charging planning module collects the vehicle speed, remaining power and vehicle location. When the user turns on the push function, it is used to automatically perform charging planning and trigger push when the power is low.

8. A new energy vehicle charging device, based on a new energy vehicle charging management system according to any one of claims 1 to 7, characterized in that: Including charging stations; A power input module, which is installed on the charging pile and is used to receive external power, including but not limited to a power interface, a filter and a protection circuit; A power conversion and output module, which is electrically connected to the power input module and installed on the charging pile, including but not limited to a transformer, a rectifier, and an inverter assembly, and is used to convert the voltage and current of the power input into a voltage and current suitable for the charged device to charge the battery. The conversion method includes using a high-frequency switching power supply technology for a DC charging device to convert AC power into DC power, and adjusting the output voltage and current to meet the charging requirements of different devices; A battery data acquisition module is installed on the charging pile to obtain the basic data of the vehicle battery of the device and the remaining battery power information at the beginning of charging through the BMS system on the new energy vehicle; The basic battery data includes the battery model, nominal capacity, and initial internal resistance; The battery is charged and inserted at the moment of charging by using a current sensor, a voltage sensor and a temperature sensor to collect the voltage at the beginning of charging, the current at the beginning of charging, the battery temperature and the ambient temperature; A display module, which is data-connected to the battery data acquisition module and installed on the charging pile, includes a display screen and an indicator light. The display screen displays the charging progress, battery level and charging time. The indicator light can use different colors to indicate different charging states. A constant red light indicates charging, a constant green light indicates full charge, and a flashing yellow light indicates a fault. The communication module is connected to the battery data acquisition module and installed on the charging pile to transmit the data collected by the battery data acquisition module to the cloud server through remote communication.

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