Intelligent management method and system for charging station with integrated charging and stopping function

Abstract

The present invention discloses an intelligent management method and system for an integrated charging station for parking and charging, and relates to the technical field of charging station management. The method comprises: connecting to a smart parking platform of a target charging station, receiving parking time information and charging amount information; generating a first charging mode based on the parking time information and charging amount information; matching a first target parking space based on the first charging mode, generating a first optimized charging decision; charging a first electric vehicle, and monitoring the charging amount to generate an amount monitoring result; when receiving a settlement feedback instruction, correcting the parking time information, obtaining the first actual parking time, and managing the fee payment. The present invention solves the technical problems in the prior art that the charging mode and parking space allocation lack personalization, the charging efficiency is low, and the real-time monitoring and adjustment of the charging process are lacking, and achieves the personalized and precise service of parking and charging for electric vehicles, and ensures the technical effect of charging safety and high efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of charging station management, and in particular to an intelligent management method and system for a charging station with integrated charging and stopping functions. Background Art

[0002] With the increasing popularity of electric vehicles, the demand for convenient, efficient and intelligent charging and parking services has become more urgent. Traditional electric vehicles need to be charged at special charging stations. With the development of smart parking platforms, charging piles can be set up in parking spaces for ordinary parking lots. Through the API interface protocol, data connection is established with each charging pile, and the parking fee and charging fee are automatically calculated. This is a service that combines electric vehicle charging with parking space use, providing users with parking and charging services at the same time.

[0003] In the past, the charging and parking management of electric vehicles was usually extensive and lacked integration. Traditional charging stations often lack consideration for users' specific needs when allocating charging modes and parking spaces, resulting in users not being able to obtain the required charging amount within the expected parking time, or being allocated charging modes and parking spaces that are not suitable for their time needs. At the same time, the parameter settings during the charging process are mostly fixed modes, and cannot be dynamically optimized according to the real-time status of the vehicle battery and the personalized expectations of the user, which not only affects the charging efficiency, but also may pose a potential threat to battery life and safety.

[0004] The existing technology has technical problems such as lack of personalization of charging mode and parking space allocation, low charging efficiency, and lack of real-time monitoring and adjustment of the charging process. Summary of the invention

[0005] The present application provides an intelligent management method and system for an integrated charging and parking station, which is used to solve the technical problems in the prior art of lack of personalization in charging modes and parking space allocation, low charging efficiency, and lack of real-time monitoring and adjustment of the charging process.

[0006] In view of the above problems, the present application provides an intelligent management method and system for an integrated charging and stopping charging station.

[0007] In a first aspect of the present application, a smart management method for an integrated charging station is provided, the method comprising:

[0008] A smart parking platform connected to a target charging station receives first expected parking duration information and first expected charge amount information of a first electric vehicle; matches charging modes based on the first expected parking duration information and the first expected charge amount information to generate a first charging mode, wherein the charging modes include a fast charging mode and a slow charging mode; matches a first target parking space based on the first charging mode, and when the first electric vehicle arrives at the first target parking space, optimizes the charging voltage and the charging current based on the first expected parking duration information and the first expected charge amount information to generate a first optimized charging decision; controls the charging module of the first target parking space to charge the first electric vehicle based on the first optimized charging decision, and monitors the charging power to generate a power monitoring result; when the smart parking platform receives a settlement feedback instruction from the first electric vehicle, it corrects the first expected parking duration information to obtain a first actual parking duration; and inputs the power monitoring result and the first actual parking duration into a billing management module for fee payment management.

[0009] A second aspect of the present application provides an intelligent management system for an integrated charging and stopping charging station, the system comprising:

[0010] A vehicle information receiving module, the vehicle information receiving module is used to connect to the smart parking platform of the target charging station, and receive first expected parking time information and first expected charging amount information of the first electric vehicle; a first charging mode generating module, the first charging mode generating module performs charging mode matching based on the first expected parking time information and the first expected charging amount information, and generates a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode; a first optimized charging decision generating module, the first optimized charging decision generating module matches the first target parking space based on the first charging mode, and when the first electric vehicle arrives at the first target parking space, performs charging based on the first expected parking time information and the first expected charging amount information The module optimizes the charging voltage and the charging current to generate a first optimized charging decision; a power monitoring result generating module, which controls the charging module of the first target parking space to charge the first electric vehicle based on the first optimized charging decision, and performs charging power monitoring to generate a power monitoring result; a first actual parking time obtaining module, which is used to correct the first expected parking time information and obtain the first actual parking time when the smart parking platform receives the settlement feedback instruction of the first electric vehicle; and a fee payment management module, which is used to input the power monitoring result and the first actual parking time into the billing management module for fee payment management.

[0011] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0012] The smart parking platform connected to the target charging station receives the first expected parking time information and the first expected charging amount information of the first electric vehicle, and generates a first charging mode; matches the first target parking space based on the first charging mode, and generates a first optimized charging decision when the first electric vehicle arrives at the first target parking space; charges the first electric vehicle, monitors the charging power, and generates a power monitoring result; when the smart parking platform receives the settlement feedback instruction of the first electric vehicle, obtains the first actual parking time; inputs the power monitoring result and the first actual parking time into the billing management module for fee payment management. It achieves personalized and accurate services for electric vehicle parking and charging, and ensures the technical effects of charging safety and efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] 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 description of the embodiments. 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 creative work.

[0014] Figure 1 A schematic flow chart of an intelligent management method for an integrated charging station with parking and charging provided in an embodiment of the present application;

[0015] Figure 2 A schematic diagram of the structure of an intelligent management system for an integrated charging and stopping charging station provided in an embodiment of the present application.

[0016] Explanation of the reference numerals: automobile information receiving module 10 , first charging mode generating module 20 , first optimized charging decision generating module 30 , power monitoring result generating module 40 , first actual parking time acquiring module 50 , fee payment management module 60 . DETAILED DESCRIPTION

[0017] The present application provides an intelligent management method and system for an integrated charging and parking station, which is used to solve the technical problems in the prior art such as lack of personalization in charging modes and parking space allocation, low charging efficiency, and lack of real-time monitoring and adjustment of the charging process.

[0018] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] Embodiment 1

[0020] like Figure 1 As shown, the present application provides an intelligent management method for an integrated charging station, the method comprising:

[0021] Step S100: connecting to the smart parking platform of the target charging station and receiving first expected parking time information and first expected charging amount information of the first electric vehicle.

[0022] Specifically, the communication is established with the smart parking platform of the target charging station through network connection, and data connection is established with each charging pile, and the integrated calculation of parking fees and charging fees is automatically performed, combining the services of electric vehicle charging and parking space use, and providing users with parking and charging services at the same time. This connection process ensures that information can be smoothly transmitted between the system and the platform. When the first electric car enters and connects to the smart parking platform of the target charging station, the user of the vehicle will enter the first expected parking time information and the first expected charging amount information on the platform according to his own itinerary and the power demand of the vehicle. The first expected parking time information reflects the time the user expects the vehicle to stay at the charging station, which is affected by factors such as the user's subsequent itinerary and tolerance for waiting time. For example, the user plans to stay for 2 hours so that the vehicle can be charged and some personal affairs can be handled during this time. The first expected charging amount information reflects the amount of electricity that the user wants to replenish for the vehicle, which depends on the current power level of the vehicle, the remaining cruising range, and the user's subsequent driving needs. For example, the user hopes to charge the vehicle to 80% to meet the next long-distance driving. After receiving this information, the smart parking platform will transmit it to the relevant management system to provide basic data for subsequent operations such as charging planning and parking space arrangement.

[0023] Step S200: performing charging mode matching based on the first expected parking duration information and the first expected charging amount information to generate a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode.

[0024] Specifically, the first expected parking time is analyzed. If the expected parking time is short, such as only 30 minutes, and the expected charging amount is large, in order to meet the charging demand as much as possible within a limited time, the fast charging mode is preferred. Because the fast charging mode can inject a large amount of electrical energy into the electric vehicle in a short time. On the contrary, if the expected parking time is long, such as several hours, and the expected charging amount is relatively small or moderate, the slow charging mode is preferred. Although the slow charging mode has a slow charging speed, it has a small loss on the battery, which is conducive to extending the battery life. Combined with the first expected charging amount information, if the expected charging amount is large and the user's expected parking time can also support the time required for fast charging, then the fast charging mode will be a better choice, but if the expected charging amount is not particularly urgent and huge, and the parking time is sufficient, the slow charging mode is more appropriate. By weighing and calculating these two key factors, a specific first charging mode is finally generated for the first electric vehicle. This mode is either a fast charging mode that can charge quickly, or a slow charging mode that is more friendly to the battery but slightly slower. For example, if the user's first expected parking time is 1 hour and the first expected charging amount is 80% of the battery capacity, the fast charging mode may be determined to be the best choice after analysis to meet the user's charging needs in a shorter time. The fast charging and slow charging resources of the charging station are effectively allocated, avoiding waste or unreasonable use of resources, and ensuring charging safety and efficiency.

[0025] Step S300: Matching a first target parking space based on the first charging mode, when the first electric vehicle arrives at the first target parking space, optimizing the charging voltage and charging current based on the first expected parking time information and the first expected charging amount information, and generating a first optimized charging decision.

[0026] Specifically, the first electric vehicle is matched with the corresponding first target parking space based on the first charging mode determined previously. This matching process will take into account the types of charging facilities equipped in different parking spaces in the charging station and their current usage status. For example, if the first charging mode determined is the fast charging mode, the vehicle will be preferentially matched with parking spaces equipped with fast charging facilities and currently idle. When the first electric vehicle arrives at the designated first target parking space, it is connected to its battery management system to collect battery pack status data including charging and discharging voltage recording timing, charging and discharging current recording timing, and charging and discharging temperature recording timing. The collected battery pack status data is used to train and construct a charging and discharging temperature prediction model, which can predict the charging and discharging temperature of the battery according to different charging conditions. The first charging power space is matched based on the determined first charging mode. In this power space, multiple groups of candidate charging decisions are generated by randomly configuring the charging voltage and charging current. These candidate charging decisions are input into the charging and discharging temperature prediction model to obtain a set of charging temperature prediction values. Finally, based on this set, the candidate charging decision with the lowest charging temperature is selected and determined as the first optimized charging decision. This means that the selected charging decision can minimize the temperature of the battery during charging while meeting the charging requirements, reduce damage to the battery, and improve charging safety and battery life.

[0027] Step S400: Based on the first optimized charging decision, the charging module of the first target parking space is controlled to charge the first electric vehicle, and the charging power is monitored to generate a power monitoring result.

[0028] Specifically, the charging module of the first target parking space is controlled to charge the first electric vehicle according to the first optimized charging decision generated previously. This control process includes sending precise instructions to the charging module, such as setting specific charging voltage, charging current, and charging time parameters. After receiving these instructions, the charging module will provide electric energy to the electric vehicle in a prescribed manner. At the same time, during the charging process, the charging power is continuously monitored in real time, which is achieved through sensors and other devices that can accurately measure the power input into the battery of the electric vehicle. As charging proceeds, the monitoring device will continuously feed back the collected power data to the system, and the system will process and analyze these data to generate detailed power monitoring results. This power monitoring result includes information such as the amount of power that has been charged, the current charging speed, and the remaining time expected to be fully charged. Through such real-time monitoring and result generation, the system can timely grasp the progress and status of charging so as to make adjustments and optimizations when necessary, and can also provide users with accurate charging information, achieving precise control, safety assurance, optimal resource utilization, and providing data support for subsequent decisions.

[0029] Step S500: When the smart parking platform receives the settlement feedback instruction of the first electric vehicle, the first expected parking time information is corrected to obtain the first actual parking time.

[0030] Specifically, the smart parking platform is in a waiting state. Once it receives a settlement feedback instruction from the first electric car, it means that the user is ready to end the parking and charging services and settle the fees. At this time, the first expected parking time information previously provided by the user will be re-evaluated and revised. This correction process takes into account the actual parking start time and the time when the settlement feedback instruction is received, so as to accurately calculate the first actual parking time. For example, if the user initially expected to park for 2 hours, but actually parked for 2 hours and 30 minutes, then the first expected parking time will be updated from 2 hours to 2 hours and 30 minutes during the correction to obtain the first actual parking time. Through such corrections, it is possible to ensure that the parking time is recorded accurately, providing an accurate data basis for subsequent cost calculation and management.

[0031] Step S600: inputting the power monitoring result and the first actual parking duration into a billing management module for fee payment management.

[0032] Specifically, when the charging of the first electric vehicle is completed, the power monitoring result and the first actual parking time are input into the billing management module. In this process, it is necessary to consider the special rules uploaded in advance by the charging station manager, that is, the parking fee discount and automatic exemption function that the owner can enjoy after the charging is completed. This rule is not fixed, but is set by the charging station manager according to the actual operation situation and strategy, and uploaded to the system. After receiving these data and rules, the computer system will calculate and manage the fees according to the established algorithm and logic. In this way, it can not only meet the flexible operation and management needs of the charging station, but also provide accurate and reasonable fee payment management services for electric vehicle owners. By setting charging piles in parking spaces for ordinary parking lots, establishing data docking with each charging pile through the API interface protocol, automatically calculating the integration of parking fees and charging fees, combining the services of electric vehicle charging and parking space use, providing users with parking and charging services at the same time, and realizing the technical effects of intelligent management and enhanced system compatibility.

[0033] In a possible implementation, step S200 further includes:

[0034] Step S210: Obtain a fast charging power space corresponding to the fast charging mode and a slow charging power space corresponding to the slow charging mode.

[0035] Step S220: Perform charging fitting in the slow charging power space based on the first expected charging amount information to generate a minimum charging fitting time.

[0036] Step S230: After superimposing the preset tolerance compensation time on the minimum charging fitting time, determine whether the first expected parking time information is met. If so, the first charging mode is a slow charging mode; if not, the first charging mode is a fast charging mode.

[0037] Specifically, the power space information corresponding to the fast charging mode and the slow charging mode will be obtained from the relevant database, configuration file or other storage medium. For the fast charging mode, the fast charging power space obtained refers to the voltage and current range that can be selected under fast charging conditions. This range usually has a higher upper limit value, the voltage is several hundred volts or even higher, and the current is relatively large. For example, the voltage is between 300V-800V, and the current is between 100A-300A. For the slow charging mode, the slow charging power space obtained is the voltage and current range that can be selected under slow charging conditions. Compared with fast charging, the values ​​in this range are usually smaller, the voltage is generally around tens of volts to two hundred volts, and the current may be between 10A-50A. By accurately obtaining this power space information, the system can provide basic data for subsequent charging mode matching and decision-making to ensure that the most suitable charging mode is selected to meet user needs.

[0038] First, the specific value of the first expected charge amount is obtained, such as 50 kWh. Then, based on the slow charging power space obtained previously, this space contains a series of possible slow charging voltage and current value ranges. Genetic algorithm is used to explain this calculation process, and the voltage and current combination in the slow charging power space is encoded into a gene sequence. Each gene sequence represents a voltage and current combination scheme. Then, a batch of initial gene sequences, that is, the first generation of "population", is randomly generated. For each gene sequence (i.e., voltage and current combination scheme), the time required to reach the first expected charge amount under this scheme is calculated, which is used as the fitness value to evaluate the pros and cons of the gene sequence. The smaller the fitness value, the better the scheme. Next, the next generation of population is generated through selection, crossover and mutation operations. The selection operation is to select excellent gene sequences to enter the next generation according to the fitness value. The crossover operation is to exchange some fragments of two excellent gene sequences to generate a new gene sequence. The mutation operation is to randomly change certain genes in the gene sequence to increase the diversity of the population. By repeating the above process, the gene sequence in the population will gradually optimize and find a better voltage and current combination scheme. After many generations of evolution, the gene sequence with the smallest fitness value is finally found, that is, the voltage and current combination scheme with the shortest time required to reach the desired charging amount. The time calculated at this time is the minimum charging fitting time.

[0039] The calculated minimum charging fitting time is added with a pre-set tolerance compensation time. This tolerance compensation time is to take into account some possible unexpected situations or small errors to ensure that the actual charging can be completed more safely. Then, the superimposed time is compared with the first expected parking time information provided by the user. If the superimposed time is less than or equal to the first expected parking time, it means that the slow charging mode can meet the charging demand within the user's expected parking time. Since the slow charging mode is less harmful to the battery, in this case, the first charging mode will be determined as the slow charging mode, which not only meets the user's needs, but also protects the battery to the greatest extent and prolongs the battery life. On the contrary, if the superimposed time is greater than the first expected parking time, it means that the slow charging mode cannot complete the charging within the user's expected time. In order to ensure that the user can obtain enough power on time, the first charging mode will be determined as the fast charging mode to complete the charging at a faster speed.

[0040] In a possible implementation, step S300 further includes:

[0041] Step S310: Read a set of free parking spaces through the smart parking platform, and any parking space in the set of free parking spaces carries charging mode identification information.

[0042] Step S320: Acquire a parking space matching the first charging mode in combination with the charging mode identification information, and generate the first target parking space.

[0043] Specifically, a connection is established with the smart parking platform and a read request is issued, and the smart parking platform will feedback the set of parking spaces that are currently idle. Each idle parking space is assigned charging mode identification information, which clearly indicates the specific charging mode that the parking space can support. These identification information are presented in the form of digital codes, specific characters, or clear text descriptions. For example, "1" represents fast charging mode, "2" represents slow charging mode, and "3" represents both fast charging and slow charging. In this way, the system can fully and accurately understand the charging mode attributes of each idle parking space, providing key basic data for subsequent parking space matching and allocation.

[0044] Analyze the charging mode identification information obtained from the smart parking platform. First, clarify the first charging mode that has been determined, such as the fast charging mode, and then check the charging mode identification of each vacant parking space one by one. If the identification of a parking space matches the first charging mode, for example, the parking space identification shows that it supports the fast charging mode, and the first charging mode is fast charging, then this parking space is selected. Among all the matching parking spaces, the final parking space is further screened and determined based on some preset rules, such as the distance from the entrance, the convenience of the parking space location, and other factors. The final determined parking space is generated and designated as the first target parking space, providing a clear parking and charging location for the upcoming vehicles.

[0045] In a possible implementation, step S300 further includes:

[0046] Step S330: connecting to the battery management system of the first electric vehicle to collect battery pack status data of the first electric vehicle, wherein the battery pack status data includes a charging and discharging voltage recording sequence, a charging and discharging current recording sequence, and a charging and discharging temperature recording sequence.

[0047] Step S340: using the battery pack status data to train and construct a charge and discharge temperature prediction model.

[0048] Step S350: matching a first charging power space based on the first charging mode.

[0049] Step S360: Randomly configure the charging voltage and the charging current in the first charging power space to generate multiple groups of candidate charging decisions.

[0050] Step S370: inputting the multiple groups of candidate charging decisions into the charging and discharging temperature prediction model to generate a set of charging temperature prediction values.

[0051] Step S380: Based on the charging temperature prediction value set, select a candidate charging decision with the minimum charging temperature to generate the first optimized charging decision.

[0052] Specifically, a communication connection is established with the battery management system of the first electric vehicle through a specific communication protocol and interface. Once the connection is successful, rich and detailed battery pack status data can be obtained from the battery management system. These data are recorded in chronological order. The charge and discharge voltage recording sequence reflects the voltage change of the battery at different time points. For example, it shows that the voltage is 300 volts at a certain moment and becomes 320 volts after a period of time. The charge and discharge current recording sequence records the flow of current over time. For example, the current is 10 amperes at the beginning and may increase to 15 amperes later. The charge and discharge temperature recording sequence tracks the temperature change of the battery during the charge and discharge process, from the initial 25 degrees Celsius to 35 degrees Celsius, etc. By obtaining these status data containing time series information, it is possible to fully understand the historical working status and performance change trends of the battery, providing a solid data foundation for subsequent analysis, prediction and optimization of charging decisions.

[0053] The multi-layer perceptron (MLP) neural network algorithm is used to build a charge and discharge temperature prediction model. The collected battery pack status data is preprocessed, including data cleaning, normalization and other operations, and the charge and discharge voltage record time series, charge and discharge current record time series and charge and discharge temperature record time series are organized into feature matrix X and target vector Y respectively. A multi-layer perceptron model is constructed, and a multi-layer perceptron with one input layer, two hidden layers and one output layer is constructed. The number of neurons in the input layer depends on the dimension of the input features, such as the combined dimension of the voltage and current time series data. The first hidden layer can be set to, for example, 128 neurons, using the ReLU activation function. The second hidden layer can be set to 64 neurons, also using the ReLU activation function. The output layer has only one neuron for predicting temperature, and no activation function is used. During the training process, the mean square error (MSE) is used as the loss function, and the optimizer can select the Adam optimizer. Through iterative training, the weights and biases of the model are continuously adjusted to minimize the loss function. Assume that there are 1,000 sets of battery pack status data. After the model is trained through the above steps, the model can output a predicted temperature value for a new set of voltage and current time series data. For example, the input voltage and current time series data are preprocessed and input into the model. The model predicts a temperature of 45°C, while the actual temperature is 43°C. By continuously optimizing the model, such prediction errors are reduced.

[0054] The corresponding charging power space is matched according to the previously determined first charging mode (such as fast charging mode or slow charging mode). The charging power space is actually a range definition, which specifies the maximum and minimum values ​​that the voltage and current can reach in a specific charging mode. If it is a fast charging mode, the corresponding first charging power space will be larger, for example, the voltage is between 300V-800V and the current is between 100A-300A. This means that in the fast charging mode, the charging device can provide a higher voltage and a larger current, thereby achieving fast charging. If it is a slow charging mode, the first charging power space is relatively small, with a voltage of about 220V-250V and a current of between 10A-50A. Such a power configuration can ensure that the battery is charged in a relatively gentle manner for a longer period of time. By accurately matching the first charging power space corresponding to the first charging mode, a reasonable range and limit are provided for subsequent charging decisions and parameter configurations to ensure the safety and efficiency of the charging process and the protection of the battery.

[0055] The operation is performed within the first charging power space that has been determined. First, the upper and lower limits of the charging voltage and charging current defined by the first charging power space are clarified. Then, specific values ​​are assigned to the charging voltage and charging current within this legal range by random generation. Each time a different set of voltage and current combinations is generated, a candidate charging decision is formed. This random generation process is repeated continuously to generate multiple sets of different candidate charging decisions.

[0056] By generating multiple groups of candidate charging decisions, each group of decisions contains specific charging voltage and charging current values, these candidate charging decisions are sequentially input into the previously constructed and trained charging and discharging temperature prediction model. The model will output a corresponding predicted charging temperature value for each group of candidate charging decisions based on the learned battery characteristics and the relationship between charging parameters and temperature. After all candidate charging decisions are input into the model, the series of predicted temperature values ​​obtained constitute a set of charging temperature prediction values.

[0057] All predicted temperature values ​​in this set are compared and screened to find the predicted temperature value with the smallest value. Then, the candidate charging decision corresponding to the minimum predicted temperature value is determined, and this candidate charging decision is selected as the first optimized charging decision. The purpose of this is to select the solution that is expected to minimize the battery temperature rise during the charging process from among many candidate charging decisions, thereby achieving safer, more efficient, and less damaging charging to the battery.

[0058] In a possible implementation, step S400 further includes:

[0059] Step S410: monitor the power level of the battery pack of the first electric vehicle in real time to generate a first real-time charging level.

[0060] Step S420: Based on the first real-time charging amount and the first optimized charging decision, charging fitting is performed with the first expected charging amount information as a target to obtain a fitting time.

[0061] Step S430: determine whether the fitting duration exceeds the first expected parking duration information, and if so, optimize the charging voltage and charging current again to generate a second optimized charging decision.

[0062] Step S440: performing charging control based on the second optimized charging decision.

[0063] Specifically, by connecting to the battery management system of the electric vehicle, the relevant data of the battery pack power is continuously obtained, and the current power information of the battery is read at a certain time interval (for example, every few minutes). This information includes the remaining capacity of the battery, the charged amount, the charging status, etc. Then, the first real-time charging amount is obtained by calculation and conversion based on the read data. This real-time charging amount can be expressed as a percentage (such as 30% charged), a specific power value (such as 15 kWh charged) or other suitable measurement methods. By real-time monitoring and generating the first real-time charging amount, the charging progress can be understood in a timely manner, providing the latest information basis for subsequent decision-making and control.

[0064] First, the actual progress of the current battery charging reflected by the first real-time charging amount is clarified. For example, the first real-time charging amount shows that the battery has been charged to 35%. According to the first optimized charging decision, which contains specific and detailed charging parameters, such as a specific stable charging voltage value (such as 380V), a charging current value (such as 60A), and the existing phased adjustment strategy, the first expected charging amount information (such as charging the battery to 85%) is used as the final goal to be achieved. According to the charging voltage and charging current in the first optimized charging decision, combined with the characteristics of the battery (such as battery capacity, internal resistance, etc.), the proportion of the increase in charging amount per unit time is calculated. This requires reference to the battery's technical specification manual or experimental data to obtain a rough charging rate. Subtract the first real-time charging amount from the first expected charging amount to obtain the amount of electricity that needs to be charged to the battery, and divide the remaining charging amount by the charging rate to obtain the fitting time.

[0065] Compare the previously calculated fitting duration with the first expected parking duration. If the fitting duration is less than or equal to the first expected parking duration, it means that the current charging solution can complete charging within the expected parking time and no further adjustment is required. However, if the fitting duration exceeds the first expected parking time, it means that the existing first optimized charging decision cannot meet the user's need to complete charging within the expected parking time. At this point, it is necessary to optimize the charging voltage and charging current again to find a new combination of charging voltage and charging current that is more suitable for the current situation, thereby generating a second optimized charging decision.

[0066] Once the second optimized charging decision is generated, the charging process will be directly controlled according to this new decision, and key parameters such as charging voltage and charging current specified in the second optimized charging decision will be passed to the charging device. After receiving these instructions, the charging device will adjust its output voltage and current values ​​accordingly to charge the battery of the electric vehicle according to the new optimization plan. During the entire charging process, the charging status will be continuously monitored to ensure that the charging device strictly follows the second optimized charging decision and can handle abnormal situations in a timely manner, such as voltage or current fluctuations beyond the allowable range, battery temperature is too high, etc.

[0067] In a possible implementation, step S430 further includes:

[0068] Step S431: Compare the first real-time charging capacity with the first expected charging capacity information to generate a second expected charging capacity.

[0069] Step S432: taking the two desired charging amounts as targets, optimizing the charging voltage and the charging current within the first charging power space, and generating an optimized charging decision.

[0070] Step S433: extracting the power conversion amplitude corresponding to the power conversion node based on the optimal charging decision.

[0071] Step S434: performing phased incremental optimization based on the power conversion amplitude, and optimizing through the charging decision feedback network to generate the second optimized charging decision.

[0072] Specifically, first obtain the specific value of the first real-time charge amount, such as 50%. At the same time, clarify the first expected charge amount information, assuming it is 90%. Then, by calculating the difference between the two, that is, 90%-50%=40%, this 40% is the generated second expected charge amount, which means that in order to reach the first expected charge amount, another 40% of the power needs to be charged on this basis.

[0073] The first charging power space defines the feasible range of charging voltage and charging current. Assuming that this range is charging voltage between 200V and 500V, and charging current between 50 and 150A, search and calculate within this specified range, and try different combinations of charging voltage and charging current. For each combination, evaluate and predict based on factors such as battery characteristics, charging efficiency, and expected charging time. Through a large number of calculations and comparisons, find an optimal charging voltage and charging current combination that can achieve the second expected charging amount within a reasonable time while meeting various charging constraints (such as battery safety, life impact, etc.). Ultimately, this optimal combination is determined as the optimal charging decision.

[0074] Assume that the charging voltage determined by the optimal charging decision is 450V and the charging current is 100A. According to the power calculation formula: power = voltage × current, the charging power at this time is 45kW. In the actual charging process, it is usually divided into different stages, such as the constant current charging stage at the beginning, and then as the battery voltage gradually rises, it will enter the constant voltage charging stage. In the constant current charging stage, the power is relatively stable, but when entering the constant voltage charging stage, the current will gradually decrease to maintain the voltage constant, resulting in a change in power. Assume that after a certain period of charging, the constant current stage switches to the constant voltage stage. This switching point is a power conversion node. In the constant current stage, the power remains at 45kW. After switching to the constant voltage stage, the current gradually decreases to 80A, and the new power becomes 36kW. Then, at this power conversion node, the power changes from 45kW to 36kW, and the power conversion amplitude is 45kW-36kW=9kW. By monitoring and calculating each such key node in the entire charging process, the power conversion amplitude corresponding to each power conversion node can be accurately extracted. These specific power conversion amplitude data can help us better understand the dynamic changes in the charging process and provide very valuable information for subsequent optimization and adjustment. For example, the charging strategy can be adjusted according to these amplitudes to make charging more efficient and safer, while also better protecting the battery life.

[0075] According to the power conversion amplitude extracted in the previous steps, such as from 40kW to 35kW, etc., the phased incremental optimization based on these amplitudes means not simply maintaining the original power change mode, but increasing the power in a planned and gradual manner. For example, if it was originally reduced from 40kW to 35kW, now the power is increased from 35kW to 38kW at the appropriate stage. At the same time, optimization is performed through the charging decision feedback network, which collects various data during the charging process in real time, including battery voltage, current, temperature, charging time, etc. According to these feedback data, the phased incremental strategy is evaluated and adjusted. If the incremental power causes the battery temperature to rise too quickly or other abnormal conditions occur, the feedback network will send a signal to adjust the incremental amplitude or suspend the incremental increase. After many such adjustments and optimizations, a more reasonable and efficient charging decision is finally generated, that is, the second optimized charging decision.

[0076] In a possible implementation, step S434 further includes:

[0077] Step S4341: performing charging capacity prediction at the first monitoring node according to the first optimized charging decision to generate a first predicted charging capacity.

[0078] Step S4342: Establishing a charging decision feedback network based on the first predicted charging amount and the first real-time charging amount.

[0079] Specifically, there has been a first optimized charging decision, which includes specific charging voltage, charging current and other parameters. The first monitoring node is a pre-set time point or charging stage. When reaching this first monitoring node, calculations and predictions will be made based on the charging parameters specified in the first optimized charging decision, as well as the characteristics of the battery and the charging model. For example, assuming that the initial charge of the battery is 10%, according to the first optimized charging decision, the charging voltage is 300V, the charging current is 80A, and it is known that 20% of the charge can be charged per hour under this charging condition. If the first monitoring node is set to 1 hour after the start of charging, then through calculation and prediction, the first predicted charge at this monitoring node should be 10%+20%=30%.

[0080] The two key data, the first predicted charging amount and the first real-time charging amount, have been obtained. The first predicted charging amount is the theoretically achievable charging amount calculated at a specific first monitoring node based on the first optimized charging decision and the related charging model, while the first real-time charging amount is the actual battery charging amount measured at the same monitoring node. The process of establishing a charging decision feedback network is as follows: compare the two quantities and calculate the difference between the first predicted charging amount and the first real-time charging amount. If the difference is positive, it means that the actual charging amount is lower than the predicted value; if the difference is negative, it means that the actual charging amount exceeds the predicted value; if the difference is zero, it means that the actual charging amount is completely consistent with the predicted value. Information collection and analysis, collect and organize this difference as well as the first predicted charging amount, the first real-time charging amount, the current charging parameters (such as charging voltage, current), the battery temperature, the charging time and other related information. Feedback mechanism establishment, based on the collected information, establish a set of rules and algorithms to determine whether the current charging situation is ideal. If the difference is large, it means that there are problems in the charging process, such as low charging efficiency, abnormal battery performance or unreasonable charging parameter settings. Decision adjustment, based on the judgment results of the feedback mechanism, adjust the subsequent charging decisions. For example, if the actual charge amount is lower than the predicted value, the charging voltage or current is appropriately increased; if the actual charge amount exceeds the predicted value too much, in order to avoid damage to the battery, the charging parameters will be reduced. Dynamic optimization, the charging decision feedback network is a dynamic system that continuously collects new data as the charging process progresses, and adjusts and optimizes the charging decision in real time based on these data to ensure the efficiency, safety and minimum damage to the battery during the charging process. For example, assume that the first predicted charge amount is 40%, while the first real-time charge amount is only 35%, with a difference of 5%. After the feedback network collects these data, it is analyzed that the current charging current may be low, resulting in a slow charging speed, so it is decided to increase the charging current from 80A to 90A to improve the subsequent charging efficiency. In the subsequent charging process, continue to monitor and compare the predicted value and the real-time value, and continuously adjust and optimize. In this way, the established charging decision feedback network can flexibly adjust the charging strategy according to the actual situation to achieve the best charging effect.

[0081] Embodiment 2

[0082] Based on the same inventive concept as the intelligent management method of an integrated charging station in the aforementioned embodiment, Figure 2 As shown, the present application provides an intelligent management system for an integrated charging station. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0083] The vehicle information receiving module 10 is used to connect to the smart parking platform of the target charging station and receive the first expected parking time information and the first expected charging amount information of the first electric vehicle.

[0084] The first charging mode generating module 20 performs charging mode matching based on the first expected parking duration information and the first expected charging amount information to generate a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode.

[0085] The first optimized charging decision generating module 30 matches the first target parking space based on the first charging mode. When the first electric vehicle arrives at the first target parking space, the charging voltage and the charging current are optimized based on the first expected parking time information and the first expected charging amount information to generate a first optimized charging decision.

[0086] The power monitoring result generating module 40 controls the charging module of the first target parking space to charge the first electric vehicle based on the first optimized charging decision, monitors the charging power, and generates a power monitoring result.

[0087] The first actual parking time acquisition module 50 is used to correct the first expected parking time information and acquire the first actual parking time when the smart parking platform receives the settlement feedback instruction of the first electric vehicle.

[0088] The fee payment management module 60 is used to input the power monitoring result and the first actual parking time into the billing management module for fee payment management.

[0089] Furthermore, the first charging mode generating module 20 further includes:

[0090] A power space acquisition unit, the power space acquisition unit is used to acquire the fast charging power space corresponding to the fast charging mode and the slow charging power space corresponding to the slow charging mode; a minimum charging fitting time generation unit, the minimum charging fitting time generation unit performs charging fitting in the slow charging power space based on the first expected charging amount information to generate a minimum charging fitting time; a parking time judgment unit, the parking time judgment unit is used to superimpose a preset tolerance compensation time on the minimum charging fitting time, and then judge whether the first expected parking time information is met. If so, the first charging mode is the slow charging mode, and if not, the first charging mode is the fast charging mode.

[0091] Furthermore, the first optimized charging decision generating module 30 further includes:

[0092] An idle parking space set reading unit, the idle parking space set reading unit is used to read the idle parking space set through the smart parking platform, and any parking space in the idle parking space set carries charging mode identification information; a first target parking space generation unit, the first target parking space generation unit is used to obtain a parking space matching the first charging mode in combination with the charging mode identification information, and generate the first target parking space.

[0093] Furthermore, the first optimized charging decision generating module 30 further includes:

[0094] a battery pack status data acquisition unit, the battery pack status data acquisition unit is used to connect to the battery management system of the first electric vehicle to collect the battery pack status data of the first electric vehicle, wherein the battery pack status data includes a charging and discharging voltage recording time series, a charging and discharging current recording time series and a charging and discharging temperature recording time series; a charging and discharging temperature prediction model construction unit, the charging and discharging temperature prediction model construction unit is used to train and construct a charging and discharging temperature prediction model using the battery pack status data; a first charging power space matching unit, the first charging power space matching unit matches the first charging power space based on the first charging mode; a candidate charging decision generation unit, the candidate charging decision generation unit is used to randomly configure the charging voltage and the charging current in the first charging power space to generate multiple groups of candidate charging decisions; a charging temperature prediction value set generation unit, the charging temperature prediction value set generation unit is used to input the multiple groups of candidate charging decisions into the charging and discharging temperature prediction model to generate a charging temperature prediction value set; a first optimized charging decision generation unit, the first optimized charging decision generation unit selects a candidate charging decision with the minimum charging temperature based on the charging temperature prediction value set to generate the first optimized charging decision.

[0095] Furthermore, the power monitoring result generating module 40 further includes:

[0096] A first real-time charging quantity generating unit, the first real-time charging quantity generating unit is used to monitor the power level of the battery pack of the first electric vehicle in real time and generate a first real-time charging quantity; a fitting time acquisition unit, the fitting time acquisition unit is based on the first real-time charging quantity and the first optimized charging decision, takes the first expected charging quantity information as a target, performs charging fitting, and obtains a fitting time; a second optimized charging decision generating unit, the second optimized charging decision generating unit is used to determine whether the fitting time exceeds the first expected parking time information, and if so, performs re-optimization of the charging voltage and charging current to generate a second optimized charging decision; a charging control unit, the charging control unit is used to perform charging control based on the second optimized charging decision.

[0097] Furthermore, the second optimized charging decision generating unit further includes:

[0098] A second expected charge amount generating unit, the second expected charge amount generating unit is used to compare the first real-time charge amount with the first expected charge amount information to generate a second expected charge amount; an optimal charging decision generating unit, the optimal charging decision generating unit is used to optimize the charging voltage and charging current in the first charging power space with the two expected charge amounts as targets, and generate an optimal charging decision; a power conversion amplitude extraction unit, the power conversion amplitude extraction unit extracts the power conversion amplitude corresponding to the power conversion node based on the optimal charging decision; a charging decision optimization unit, the charging decision optimization unit performs phased incremental optimization based on the power conversion amplitude, and optimizes through the charging decision feedback network to generate the second optimized charging decision.

[0099] Furthermore, the charging decision optimization unit also includes:

[0100] A first predicted charging amount generating unit, the first predicted charging amount generating unit is used to perform charging amount prediction at the first monitoring node according to the first optimized charging decision to generate a first predicted charging amount; a charging decision feedback network establishing unit, the charging decision feedback network establishing unit establishes a charging decision feedback network based on the first predicted charging amount and the first real-time charging amount.

[0101] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0102] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0103] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent management method for an integrated charging and stopping charging station, characterized in that: include: A smart parking platform connected to a target charging station receives first expected parking time information and first expected charging amount information of a first electric vehicle; Performing charging mode matching based on the first expected parking duration information and the first expected charging amount information to generate a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode; Based on the first charging mode matching the first target parking space, when the first electric vehicle arrives at the first target parking space, optimizing the charging voltage and the charging current based on the first expected parking time information and the first expected charging amount information, and generating a first optimized charging decision; Based on the first optimized charging decision, controlling the charging module of the first target parking space to charge the first electric vehicle, and monitoring the charging power to generate a power monitoring result; When the smart parking platform receives the settlement feedback instruction of the first electric vehicle, the first expected parking time information is corrected to obtain the first actual parking time; Inputting the power monitoring result and the first actual parking time into a billing management module for fee payment management; Optimizing the charging voltage and the charging current based on the first expected parking time information and the first expected charging amount information to generate a first optimized charging decision, including: Connecting to the battery management system of the first electric vehicle to collect battery pack status data of the first electric vehicle, wherein the battery pack status data includes a charging and discharging voltage recording timing sequence, a charging and discharging current recording timing sequence, and a charging and discharging temperature recording timing sequence; The battery pack status data is used to train and construct a charge and discharge temperature prediction model; Matching a first charging power space based on the first charging mode; Randomly configuring the charging voltage and the charging current in the first charging power space to generate multiple groups of candidate charging decisions; Inputting the plurality of groups of candidate charging decisions into the charging and discharging temperature prediction model to generate a set of charging temperature prediction values; Based on the charging temperature prediction value set, selecting a candidate charging decision with the minimum charging temperature, and generating the first optimized charging decision; After controlling the charging module of the first target parking space to charge the first electric vehicle based on the first optimized charging decision, the method further includes: Performing real-time monitoring of the power level of the battery pack of the first electric vehicle to generate a first real-time charging level; Based on the first real-time charging amount and the first optimized charging decision, taking the first expected charging amount information as a target, performing charging fitting to obtain fitting time; Determine whether the fitting duration exceeds the first expected parking duration information, and if so, optimize the charging voltage and charging current again to generate a second optimized charging decision; performing charging control based on the second optimized charging decision; The charging voltage and the charging current are optimized again to generate a second optimized charging decision, including: comparing the first real-time charging amount with the first expected charging amount information to generate a second expected charging amount; Taking the second desired charging amount as a target, optimizing the charging voltage and the charging current within the first charging power space, and generating an optimal charging decision; Extracting the power conversion amplitude corresponding to the power conversion node based on the optimal charging decision; Performing phased incremental optimization based on the power conversion amplitude and optimizing through a charging decision feedback network to generate the second optimized charging decision; Performing a charge capacity prediction at a first monitoring node according to the first optimized charging decision to generate a first predicted charge capacity; A charging decision feedback network is established based on the first predicted charging amount and the first real-time charging amount.

2. The intelligent management method of the integrated charging and stopping charging station according to claim 1, characterized in that: Based on the first expected parking duration information and the first expected charging amount information, a charging mode is matched to generate a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode, including: Obtaining a fast charging power space corresponding to the fast charging mode and a slow charging power space corresponding to the slow charging mode; Performing charging fitting in the slow charging power space based on the first expected charging amount information to generate a minimum charging fitting time; After the preset tolerance compensation time is superimposed on the minimum charging fitting time, it is determined whether the first expected parking time information is met. If so, the first charging mode is a slow charging mode, and if not, the first charging mode is a fast charging mode.

3. The intelligent management method of the integrated charging and stopping charging station according to claim 1, characterized in that: Matching a first target parking space based on the first charging mode includes: reading a set of free parking spaces through a smart parking platform, wherein any parking space in the set of free parking spaces carries charging mode identification information; A parking space matching the first charging mode is acquired in combination with the charging mode identification information to generate the first target parking space.

4. An intelligent management system for an integrated charging and stopping charging station, characterized in that: Steps for implementing the intelligent management method of an integrated charging station according to any one of claims 1 to 3, the system comprising: A vehicle information receiving module, wherein the vehicle information receiving module is used to connect to a smart parking platform of a target charging station and receive first expected parking duration information and first expected charging amount information of a first electric vehicle; a first charging mode generating module, wherein the first charging mode generating module performs charging mode matching based on the first expected parking duration information and the first expected charging amount information to generate a first charging mode, wherein the charging mode includes a fast charging mode and a slow charging mode; a first optimized charging decision generating module, wherein the first optimized charging decision generating module matches a first target parking space based on the first charging mode, and when the first electric vehicle arrives at the first target parking space, the first optimized charging decision generating module optimizes the charging voltage and the charging current based on the first expected parking time information and the first expected charging amount information; A power monitoring result generating module, wherein the power monitoring result generating module controls the charging module of the first target parking space to charge the first electric vehicle based on the first optimized charging decision, and performs charging power monitoring to generate a power monitoring result; A first actual parking time acquisition module, wherein the first actual parking time acquisition module is used to correct the first expected parking time information and acquire the first actual parking time when the smart parking platform receives the settlement feedback instruction of the first electric vehicle; A fee payment management module is used to input the power monitoring result and the first actual parking time into a billing management module for fee payment management.

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