Intelligent control method and system for reservation charging of electric vehicle and electronic equipment

Abstract

The invention relates to the technical field of electric vehicle charging, in particular to an electric vehicle reservation charging intelligent control method and system and electronic device.The method comprises the steps that in response to a target charging request initiated by a user, battery health state data and electricity market price data of a target vehicle are obtained according to the target charging request; determining a charging power upper limit constraint value according to the battery health state data; generating a charging power curve based on the charging power upper limit constraint value in combination with the target charging request and the electricity market price data; based on the charging power curve, a charging instruction is generated, and the charging instruction is used for controlling a charging pile to charge the target vehicle. The method has the advantages that the battery health of the electric vehicle is effectively guaranteed while the charging cost of the user is reduced, and therefore the satisfaction degree of the user is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric vehicle charging, in particular to an electric vehicle reservation charging intelligent control method and system and electronic equipment. BACKGROUND

[0002] At present, with the popularization of electric vehicles, charging piles have become an important infrastructure. Users charge electric vehicles through charging piles to meet daily travel needs. In order to improve charging efficiency and convenience, some charging reservation management systems have appeared, allowing users to reserve charging time and charging piles.

[0003] The existing electric vehicle charging control method usually adopts a relatively simple strategy. One is the plug-and-charge mode, and the charging process is started at the maximum power immediately after the user connects the vehicle until it is fully charged. Another is a simple timing reservation, and the user presets a fixed start charging time such as a low valley power period at night. When these methods perform charging, they usually charge the vehicle according to a fixed process and do not adjust according to factors such as power grid load and charging pile usage, which can easily cause resource over-concentration during the charging peak period, long charging waiting time, and ineffective use of low valley power periods, increasing the burden on the power grid, thereby reducing charging efficiency and user satisfaction, and thus there is room for improvement. SUMMARY

[0004] The present application provides an electric vehicle reservation charging intelligent control method, system and electronic equipment, which can reduce the charging cost of users while effectively protecting the battery health of electric vehicles, thereby improving user satisfaction.

[0005] The above-mentioned invention purpose of the present application is achieved by the following technical scheme: An electric vehicle reservation charging intelligent control method, the electric vehicle reservation charging intelligent control method comprising: In response to a target charging request initiated by a user, obtaining battery health state data and power market price data of a target vehicle according to the target charging request; Determining a charging power upper limit constraint value according to the battery health state data; Generating a charging power curve based on the charging power upper limit constraint value, in combination with the target charging request and the power market price data; Generating a charging instruction based on the charging power curve, the charging instruction being used to control a charging pile to charge the target vehicle.

[0006] By adopting the above technical solution, and responding to user-initiated target charging requests, battery health status data and electricity market price data are obtained. Based on real-time battery health status data and electricity market price data, the blindness of traditional charging is eliminated, providing a foundation for subsequent dual optimization of safety and economy. By determining the upper limit constraint value of charging power based on battery health status data, the complex battery health status is quantified into a clear safety boundary, thereby effectively preventing battery overheating or permanent damage caused by excessive charging power, ensuring the physical safety of the charging process. By generating a charging power curve based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a power allocation scheme with the lowest total cost can be planned under the premise of meeting the user's charging goals and safety constraints. This directly saves users charging costs without sacrificing safety and convenience. By generating charging commands based on the charging power curve to control the charging pile, the optimization results generated by the algorithm can be transformed into control actions of the equipment, thereby ensuring the execution of the entire scheduling scheme and realizing a closed loop from data to control, ensuring the battery health of electric vehicles.

[0007] In a preferred embodiment, this application can be further configured such that the electric vehicle scheduled charging intelligent control method also includes: Obtain the historical charge-discharge cycle results of the electric vehicle, and extract the battery initial state data, charging process data, and battery health state degradation amount for each charging cycle from the historical charge-discharge cycle results; Based on the battery health state decay, the battery initial state data of each charging cycle is associated and matched with the charging process data of the corresponding charging cycle to obtain the model training dataset. The initial prediction model is trained based on the training dataset to obtain the battery health prediction model.

[0008] By adopting the above technical solution, and obtaining the model training dataset by acquiring historical charge-discharge cycle results and performing correlation matching, the scattered and raw vehicle operation data can be processed into machine learning samples containing causal relationships. This provides a high-quality and large-scale data foundation for training a high-precision prediction model. By training the initial prediction model based on the training dataset, a battery health prediction model is obtained. The algorithm can learn the complex nonlinear relationship between charging behavior and battery loss, thereby obtaining an intelligent evaluation tool with generalization capabilities, which provides technical support for realizing battery maintenance-style charging.

[0009] In a preferred embodiment, this application can be further configured as follows: obtaining battery health status data and electricity market price data of the target vehicle based on the target charging request specifically includes: Obtain the vehicle identity information and time target of the target vehicle from the target charging request, and send a data request to the battery management system of the target vehicle based on the vehicle identity information to obtain the battery health status data. Based on the stated time objective, electricity market price data within the corresponding time window is obtained through the electricity trading market platform.

[0010] By adopting the above technical solution, by obtaining vehicle identity information from the target charging request and sending a data request to the target vehicle's battery management system, the most accurate battery status parameters of the target vehicle at the current moment can be obtained, thereby ensuring the accuracy of decision-making and avoiding the risks of using general or outdated data. By obtaining price data through the electricity trading market platform based on time targets, future time-of-use electricity price information can be obtained, thus providing a reliable economic basis for cost optimization calculations and enabling cost-saving strategies to be realized.

[0011] In a preferred embodiment, this application can be further configured such that: determining the upper limit constraint value of charging power based on the battery health status data specifically includes: Based on the battery health status data, the battery safety status parameters of the target vehicle are obtained, including the real-time battery temperature, battery internal resistance, and state of charge. Multiple preset candidate charging power values ​​are obtained, the battery safety state parameter is combined with the multiple candidate charging power values, and each combination is input into the battery health prediction model to obtain multiple corresponding expected battery health state degradation amounts. The expected battery health degradation is compared with a preset loss threshold. The optimal candidate charging power value that does not exceed the preset loss threshold is selected from the comparison results, and the optimal candidate charging power value is used as the upper limit constraint value of the charging power.

[0012] By adopting the above technical solution and obtaining battery safety status parameters, the core indicators most directly related to charging safety can be focused from the complex battery management system data, thereby improving the efficiency and accuracy of subsequent model evaluation. By obtaining multiple candidate charging power values ​​and inputting them into the battery health prediction model to obtain the expected degradation amount, parallel simulation and deduction of various charging strategies can be performed. This transforms a complex optimization problem into a decision-making problem of choosing from multiple deterministic results. By comparing the expected degradation amount with a preset loss threshold and selecting the optimal candidate charging power value as the upper limit constraint value of charging power, the final safe power decision can be made based on quantitative loss prediction and clear judgment rules, thus finding the best balance between ensuring battery health and meeting safety requirements.

[0013] In a preferred embodiment, this application can be further configured such that: the generation of a charging power curve based on the charging power upper limit constraint value, combined with the target charging request and the electricity market price data, specifically includes: The time window is divided into multiple discrete time units. Based on the electricity market price data, the multiple time units are sorted according to the charging cost order to generate a priority charging sequence. According to the priority charging sequence, charging power is allocated to the time units in sequence so that the charging power does not exceed the upper limit constraint value of the charging power, until the total amount of charging allocated meets the total amount of charging target in the target charging request. The charging power curve is generated according to each time unit and the corresponding charging power.

[0014] By adopting the above technical solution, dividing the time window into discrete time units and generating a priority charging sequence, the complex time and cost relationship can be transformed into an ordered list of action priorities. This greatly simplifies the subsequent power allocation algorithm and improves computational efficiency. By allocating charging power to low-cost time units according to the priority charging sequence until the total charging target is met, the charging task can be intelligently filled into the time period with the lowest cost while strictly adhering to safety boundaries. This ensures that among all the solutions that can meet user needs, the final charging solution generated is the one with the lowest total cost, thus maximizing economic benefits.

[0015] In a preferred embodiment, this application can be further configured as follows: Based on the charging power curve, a charging command is generated, wherein the charging command is used to control the charging pile to charge the target vehicle, specifically including: Based on the charging power curve, a charging instruction is generated according to the time unit and corresponding charging power in each charging power curve, and the target vehicle is charged according to the charging instruction.

[0016] By adopting the above technical solution and generating charging instructions based on the charging power curve, the power curve planned by the upper-level algorithm and in the form of a time series can be converted into control commands that can be recognized and executed by the underlying charging pile hardware. By charging the target vehicle according to the charging instructions, the feasibility and final effect of the entire optimization scheme are ensured, and the goal of saving costs for users and protecting the battery is truly achieved.

[0017] In a preferred embodiment, this application can be further configured such that the electric vehicle scheduled charging intelligent control method also includes: The upper limit constraint value of the charging power is used as the charging power setting value of the time unit; Calculate the charging amount for each time unit based on the charging power setting value in chronological order until the cumulative charging amount meets the total charging amount target. Generate alternative charging power curves based on each time unit and the corresponding charging power setting value. Based on the electricity market price data, the estimated total charging time and estimated total charging cost are calculated according to the charging power curve and the alternative charging power curve, respectively. The estimated total charging time and estimated total charging cost are sent to the user terminal, and a selection instruction is received from the user terminal. The selection instruction is used to determine the charging power curve required by the user.

[0018] By adopting the above technical solution and generating alternative charging power curves, charging strategies based on different optimization objectives—lowest cost and shortest time—can be generated simultaneously within a single control flow. This provides users with a clear basis for decision-making. By calculating the estimated total charging time and estimated total charging cost of the two curves separately, the two complex power curves can be quantified into the two core indicators that are most intuitive to users: total time and total cost. This makes the comparison between different strategies simple and direct. By sending the estimated results to the user terminal and receiving the user's selection instruction, the final strategy selection power can be returned to the user, greatly improving the flexibility, transparency, and user-friendliness of the charging service.

[0019] The second objective of this invention is achieved through the following technical solution: An intelligent control system for scheduled charging of electric vehicles, the intelligent control system for scheduled charging of electric vehicles includes: The data acquisition module is used to respond to a user-initiated target charging request and, based on the target charging request, acquire battery health status data and electricity market price data of the target vehicle. The constraint determination module is used to determine the upper limit constraint value of charging power based on the battery health status data; The target optimization module is used to generate a charging power curve based on the charging power upper limit constraint value, combined with the target charging request and the electricity market price data; The instruction control module is used to generate a charging instruction based on the charging power curve, and the charging instruction is used to control the charging pile to charge the target vehicle.

[0020] By adopting the above technical solution, and responding to user-initiated target charging requests, battery health status data and electricity market price data are obtained. Based on real-time battery health status data and electricity market price data, the blindness of traditional charging is eliminated, providing a foundation for subsequent dual optimization of safety and economy. By determining the upper limit constraint value of charging power based on battery health status data, the complex battery health status is quantified into a clear safety boundary, thereby effectively preventing battery overheating or permanent damage caused by excessive charging power, ensuring the physical safety of the charging process. By generating a charging power curve based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a power allocation scheme with the lowest total cost can be planned under the premise of meeting the user's charging goals and safety constraints. This directly saves users charging costs without sacrificing safety and convenience. By generating charging commands based on the charging power curve to control the charging pile, the optimization results generated by the algorithm can be transformed into control actions of the equipment, thereby ensuring the execution of the entire scheduling scheme and realizing a closed loop from data to control, ensuring the battery health of electric vehicles.

[0021] The above-mentioned objective three of this application is achieved through the following technical solution: An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described intelligent control method for scheduled charging of electric vehicles.

[0022] The fourth objective of this application is achieved through the following technical solution: A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described intelligent control method for scheduled charging of electric vehicles.

[0023] In summary, this application includes at least one of the following beneficial technical effects: 1. By responding to user-initiated target charging requests and acquiring battery health status data and electricity market price data, the system can eliminate the blindness of traditional charging based on real-time battery health status data and electricity market price data, providing a foundation for subsequent dual optimization of safety and economy. By determining the upper limit constraint value of charging power based on battery health status data, the complex battery health status is quantified into a clear safety boundary, thereby effectively preventing battery overheating or permanent damage caused by excessive charging power and ensuring the physical safety of the charging process. By generating a charging power curve based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, the system can plan the power allocation scheme with the lowest total cost while meeting the user's charging goals and safety constraints, thereby directly saving users charging costs without sacrificing safety and convenience. By generating charging commands based on the charging power curve to control the charging pile, the system can transform the optimization results generated by the algorithm into the control actions of the equipment, thereby ensuring the execution of the entire scheduling scheme and realizing a closed loop from data to control, thus ensuring the battery health of electric vehicles. 2. By generating alternative charging power curves, charging strategies based on different optimization objectives—lowest cost and shortest time—can be generated simultaneously within a single control flow, providing users with a clear decision-making basis. By calculating the estimated total charging time and estimated total charging cost of the two curves separately, the two complex power curves can be quantified into the two core indicators that are most intuitive to users: total time and total cost. This makes comparing different strategies simple and direct. By sending the estimated results to the user terminal and receiving the user's selection instructions, the final strategy selection power can be returned to the user, greatly improving the flexibility, transparency, and user-friendliness of the charging service. Attached Figure Description

[0024] Figure 1 This is a flowchart illustrating the implementation of an intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 2 This is another implementation flowchart of the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application; Figure 3 This is a flowchart illustrating the implementation of step S10 in the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 4 This is a flowchart illustrating the implementation of step S20 in the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 5 This is a flowchart illustrating the implementation of step S30 in the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 6This is a flowchart illustrating the implementation of step S40 in the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 7 This is a flowchart illustrating the implementation of step S32 and beyond in the intelligent control method for scheduled charging of electric vehicles in one embodiment of this application. Figure 8 This is a schematic block diagram of an intelligent control system for scheduled charging of electric vehicles according to one embodiment of this application; Figure 9 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0025] The following embodiments will help those skilled in the art to further understand the function of this application, but do not limit this application in any way. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application. These all fall within the protection scope of this application.

[0026] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0027] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0028] The present application will be further described in detail below with reference to the accompanying drawings.

[0029] In one embodiment, such as Figure 1 As shown, this application discloses an intelligent control method for scheduled charging of electric vehicles, which specifically includes the following steps: S10: In response to a user-initiated target charging request, obtain the target vehicle's battery health status data and electricity market price data based on the target charging request.

[0030] Specifically, the target charging request is a set of instructions set by the user through the terminal device, containing specific charging expectations, such as charging the vehicle to 80% before 7:00 AM tomorrow. Battery health status data is obtained through real-time interaction with the vehicle's battery management system (BMS), and is a series of parameters reflecting the current physical condition of the battery, such as battery temperature, internal resistance, and aging degree. It represents the vehicle's own battery health status. Electricity market price data refers to the time-of-use electricity price information obtained from the power grid or energy service provider, representing the external economic environment of charging behavior. By obtaining these two types of data, namely the target vehicle's battery health status data and electricity market price data, it is like conducting due diligence and market analysis for a smart financial investment. The purpose is to abandon the blindness of traditional charging and provide a comprehensive and real-time data foundation for subsequent decision-making to achieve optimal economic efficiency while ensuring safety.

[0031] S20: Determine the upper limit constraint value of charging power based on battery health status data.

[0032] Specifically, the upper limit constraint value of charging power is the maximum charging power value allowed to be applied under the current state, calculated based on the physical safety boundary of the battery. It is a dynamically changing safety ceiling. The determination process will take the acquired real-time battery temperature, internal resistance and other health data into a preset battery health prediction model for evaluation. For example, when the model judges that the battery temperature is too low, it may set the constraint value to a lower 30kW to prevent lithium deposition damage, while when the battery is at the optimal operating temperature, the constraint value may be set to a higher 120kW. In this way, the complex and multi-dimensional battery health problem is transformed into a single, clear and mandatory power constraint parameter. Just like a doctor gives a patient a clear maximum safe exercise heart rate based on a detailed physical examination report, it provides a hard safety guarantee for the formulation of all subsequent charging strategies.

[0033] S30: Based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a charging power curve is generated.

[0034] Specifically, the charging power curve is a time-series scheme that meticulously plans the specific charging power to be used at each point in time within the entire charging time window. It integrates information from three dimensions: the safety ceiling (charging power upper limit constraint value) determined in the preceding steps, the user's target charging request (e.g., fully charging before 7 AM tomorrow), and the external economic environment (electricity market price). This process aims to find an optimal solution that does not exceed the safety ceiling, meets the user's time requirements, and minimizes the total charging cost. For example, it maintains zero or low power during periods of high electricity prices, while charging at the maximum power allowed by the safety ceiling is used during off-peak periods of low electricity prices.

[0035] S40: Based on the charging power curve, generate charging commands, which are used to control the charging station to charge the target vehicle.

[0036] Specifically, the charging command transforms the time-series charging power curve generated in the previous step into a command that the charging pile hardware can recognize and execute. For example, the planned point on the curve at 02:30 AM, where the power is set to 80kW, will be converted into an electronic message containing a specific instruction code, address, and parameters, such as SET_POWER(TIMESTAMP=xxxx,POWER=80000), and sent to the charging pile via the communication module, thereby driving the charging pile's power module to make corresponding adjustments.

[0037] In one embodiment, such as Figure 2 As shown, the intelligent control method for scheduled charging of electric vehicles also includes: S101: Obtain the historical charge-discharge cycle results of the electric vehicle, and extract the battery initial state data, charging process data, and battery health state degradation amount for each charging cycle from the historical charge-discharge cycle results.

[0038] Specifically, the historical charge-discharge cycle results include tens of thousands of complete charging event records retrieved from the vehicle manufacturer's back-end database or battery testing platform. Each record includes at least the following: initial battery state data representing initial conditions, such as the battery temperature at the start of charging and the total mileage already driven; charging process data representing process behavior, such as the actual current and voltage values ​​per minute during charging; and the amount of battery health degradation representing the final result. This is a quantitative indicator reflecting the permanent loss of the battery, calculated by accurately measuring the minute changes in the maximum battery capacity before and after this charge.

[0039] S102: Based on the battery health state decay, the initial battery state data of each charging cycle is associated and matched with the charging process data of the corresponding charging cycle to obtain the model training dataset.

[0040] Specifically, the three types of data obtained in the previous steps are made into independent causal sample pairs. In each sample pair, the initial state of the battery and the charging process data are combined as input features to describe the cause, while the battery health state degradation corresponding to that charging is used as the training label to describe the result. For example, a sample can be represented as: when the initial temperature = 25°C and the charging power = 80kW, the corresponding degradation amount = 0.003%, thereby transforming the original business data into machine learning samples that can be used for training.

[0041] S103: Train the initial prediction model based on the training dataset to obtain the battery health prediction model.

[0042] Specifically, the training process is an iterative optimization process. The initial prediction model, such as a neural network, will continuously try to predict the results based on the input features and compare the prediction results with the real training labels to calculate the error. Then, the internal parameters of the model are continuously adjusted through optimization algorithms to make the error smaller and smaller. When the error converges to a sufficiently small range, the preset convergence condition is met, or the number of training iterations of the model meets the preset number of iterations, and the training is completed. The final battery health prediction model has the ability to make accurate damage predictions for new charging conditions.

[0043] In one embodiment, such as Figure 3 As shown, in step S10, based on the target charging request, the battery health status data and electricity market price data of the target vehicle are obtained, specifically including: S11: Obtain the vehicle identity information and time target of the target vehicle from the target charging request, and send a data request to the battery management system of the target vehicle based on the vehicle identity information to obtain battery health status data.

[0044] Specifically, the vehicle identification information, namely the vehicle unique identification code (VIN), is submitted along with the user's charging request through the terminal device. After obtaining this information, a remote data query is initiated to the vehicle corresponding to the specific VIN through the vehicle network communication gateway. This query command will pass through the communication network and reach the battery management system (BMS) inside the vehicle. After receiving the command, the BMS will package and return the most accurate battery health status data at the current moment, such as cell temperature and voltage, thereby ensuring that the formulation of all subsequent safety strategies is based on the most accurate and personalized data of this vehicle at this moment.

[0045] S12: Based on the time target, obtain the electricity market price data within the time window corresponding to the time target through the electricity trading market platform.

[0046] Specifically, firstly, a specific time window is determined from the user's target charging request, such as being fully charged before 7:00 AM tomorrow, from the current time to the deadline. Then, using this time window as a query parameter, a data request is initiated to the application programming interface (API) of the electricity trading market platform to obtain electricity price forecast data refined to the minute or hour within this specific time period, such as a continuously changing list of electricity prices with a quote every fifteen minutes. This provides accurate, reliable, and predictive price information for subsequent cost optimization calculations.

[0047] In one embodiment, such as Figure 4As shown, in step S20, the upper limit constraint value of charging power is determined based on the battery health status data, specifically including: S21: Based on the battery health status data, obtain the battery safety status parameters of the target vehicle. The battery safety status parameters include the real-time battery temperature, battery internal resistance, and current remaining charge.

[0048] Specifically, this involves extracting key information from battery health status data obtained from the vehicle's Battery Management System (BMS). Battery health is a comprehensive concept, but several core physical parameters primarily affect its charging safety. From the battery health status data reported by the BMS, we obtain the current average temperature of the battery cells, the internal resistance reflecting the degree of battery aging, and the current state of charge (SOC). For example, the real-time battery temperature directly relates to the risk of thermal runaway; the battery's internal resistance is an important basis for judging the degree of battery aging and its internal heat generation capacity during charging; and the SOC determines the battery's current ability to receive charging current. By obtaining these parameters that best reflect the battery's immediate safety status, we provide standardized, high-quality input data for the accurate prediction and evaluation of subsequent models.

[0049] S22: Obtain multiple preset candidate charging power values, combine the battery safety state parameters with the multiple candidate charging power values, input each combination into the battery health prediction model, and obtain multiple corresponding expected battery health state degradation amounts.

[0050] Specifically, this step involves a simulation using a pre-trained prediction model. The candidate charging power values ​​are a series of preset charging intensities to be evaluated, such as {10kW, 20kW, ..., 120kW}. By combining the battery safety state parameters obtained in the previous step with each candidate charging power value, multiple simulation scenarios are formed. These scenarios are then input into the battery health prediction model one by one. The model will output a quantitative prediction result for each scenario, namely the expected battery health state degradation, which indicates the degree of minor permanent damage that charging at that power may cause to the battery. By using the model for simulation, the safety risks that different charging strategies may bring are quantified.

[0051] S23: Compare the expected battery health state degradation with the preset loss threshold, select the optimal candidate charging power value that does not exceed the preset loss threshold from the comparison results, and use the optimal candidate charging power value as the upper limit constraint value of charging power.

[0052] Specifically, this step is a safety decision-making process based on the simulation results of the previous steps. The preset loss threshold is a red line set within the system, representing the maximum acceptable battery loss for this charging operation, for example, 0.002%. By comparing the expected attenuation of each candidate charging power value obtained in the previous steps with the preset loss threshold, all candidate charging power values ​​that cause attenuation exceeding the preset loss threshold will be considered unsafe and discarded. Among all the candidate charging power values ​​determined to be safe, the highest power value is selected as the optimal candidate charging power value, and it is ultimately determined as the upper limit constraint value of the charging power for this charging task. This serves as a safety ceiling that must be strictly adhered to in all subsequent economic optimizations. For example, if the preset loss threshold is 0.002%, and the losses of 50kW and 60kW are both below this value, while the losses of 70kW are above this value, then 60kW will be selected as the final result.

[0053] In one embodiment, such as Figure 5 As shown, in step S30, based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a charging power curve is generated, specifically including: S31: Divide the time window into multiple discrete time units, and based on electricity market price data, sort the multiple time units according to the charging cost order to generate a priority charging sequence.

[0054] Specifically, this step involves strategy planning and preprocessing to achieve optimal cost. The available charging time window in the user's target charging request, such as from 10 PM to 7 AM the next day, is discretized into a series of equal-length time segments, for example, 540 independent time units, each unit being 1 minute. Next, based on the acquired electricity market price data, each of these 540 time units is labeled with its corresponding charging cost, and they are sorted in order of cost from low to high. This sorted list is the final priority charging sequence, which is an intelligent action roadmap, with the time unit with the lowest electricity price at the front and the time unit with the highest electricity price at the back.

[0055] S32: According to the priority charging sequence, the charging power is allocated to the time unit in turn to ensure that the charging power does not exceed the upper limit constraint value of the charging power, until the total amount of charging allocated meets the total amount of charging target in the target charging request. A charging power curve is generated based on each time unit and the corresponding charging power.

[0056] Specifically, based on the priority charging sequence generated in the previous steps, starting from the top of the priority charging sequence, i.e. the time unit with the lowest cost, an attempt is made to allocate charging power. The allocated power value cannot exceed the upper limit constraint value of charging power, which represents the safety ceiling, determined in the previous steps. Power is filled into subsequent time units in strict accordance with the priority sequence, and the total amount of charging allocated is accumulated in real time. When the accumulated charging amount reaches the total charging amount set by the user in the target charging request, such as 50kWh, the filling process stops immediately. Those redundant time units that are further in the sequence and have higher costs will not be allocated any power. Finally, the complete time schedule composed of each time unit that has been filled with power and its corresponding power value is the final, cost-optimal charging power curve.

[0057] In one embodiment, such as Figure 6 As shown, in step S40, a charging command is generated based on the charging power curve. This charging command is used to control the charging pile to charge the target vehicle, specifically including: S41: Based on the charging power curve, generate a charging command according to the time unit and corresponding charging power in each charging power curve, and charge the target vehicle according to the charging command.

[0058] Specifically, the charging power curve is traversed for each time unit and its corresponding power value. This information is converted into a communication protocol and instruction format that can be recognized by the specific charging pile hardware, such as the OCPP protocol. This forms a series of formatted charging instructions with execution timestamps, which are then sent to the charging pile through the communication module. This drives the charging pile's power module to make corresponding adjustments, adjusting its output power at the specified time to achieve charging of the target vehicle.

[0059] In one embodiment, such as Figure 7 As shown, after step S32, the intelligent control method for scheduled charging of electric vehicles further includes: S33: Use the upper limit constraint value of charging power as the charging power setting value of the time unit.

[0060] Specifically, in order to achieve the fastest charging speed, this method completely ignores the priority charging sequence generated in the previous steps that takes into account the price factors of the electricity market. The only decision-making basis is to ensure the safety of the battery at all times. Therefore, it directly adopts the upper limit constraint value of charging power, which represents the physical limit that the battery can currently withstand, determined in the previous steps, as the target charging power setting value for the current time unit, so as to ensure that charging is carried out at the maximum safe speed at every moment.

[0061] S34: Calculate the charging amount based on the charging power setting value for each time unit in chronological order until the cumulative charging amount meets the total charging amount target. Generate alternative charging power curves based on each time unit and the corresponding charging power setting value.

[0062] Specifically, starting from the first time unit of charging, charging is continuously performed at the upper limit constraint value of charging power determined in the previous step, and the charged amount is continuously accumulated until the accumulated charging amount reaches the user's initial total charging amount target, at which point charging stops immediately. The curve composed of all time units and their corresponding charging power values ​​in this process is the final alternative charging power curve, which represents the shortest time plan required to complete this charging under the premise of ensuring safety.

[0063] S35: Based on electricity market price data, the estimated total charging time and estimated total charging cost are calculated according to the charging power curve and the alternative charging power curve, respectively.

[0064] Specifically, after generating two schemes, namely the cost-saving mode (charging power curve) and the fastest mode (alternative charging power curve), the estimated total charging time is obtained by counting the number of time units contained in each curve. The estimated total charging cost is obtained by integrating and accumulating the power value of each curve with the corresponding market electricity price over time, i.e., total cost = Σ(power_i × duration_i × electricity price_i), where i represents each time unit. Thus, two sets of clear comparative data are output, such as scheme A: 5 hours, 25 yuan, and scheme B: 3 hours, 45 yuan.

[0065] S36: Send the estimated total charging time and estimated total charging cost to the user terminal, and receive the selection instruction from the user terminal. The selection instruction is used to determine the charging power curve required by the user.

[0066] Specifically, the quantitative results of the two schemes analyzed in the previous steps are presented in an easy-to-understand way, such as economic mode and fast mode, on the user's terminal device, such as a mobile APP interface. The user can choose the former according to their actual needs at the time. For example, if they are not in a hurry, they can choose the latter. They can make a choice by clicking. Only after receiving this final selection instruction from the user's terminal will the selected power curve be used as the final scheme for subsequent charging execution.

[0067] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0068] In one embodiment, an intelligent control system for scheduled charging of electric vehicles is provided, which corresponds one-to-one with the intelligent control method for scheduled charging of electric vehicles described in the above embodiments. For example... Figure 8 As shown, the intelligent control system for scheduled charging of electric vehicles includes a data acquisition module, a constraint determination module, a target optimization module, and a command control module. Detailed descriptions of each functional module are as follows: The data acquisition module is used to respond to the user's target charging request and, based on the target charging request, acquire the battery health status data and electricity market price data of the target vehicle. The constraint determination module is used to determine the upper limit constraint value of charging power based on battery health status data; The target optimization module is used to generate a charging power curve based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data; The command control module is used to generate charging commands based on the charging power curve. These commands are then used to control the charging station to charge the target vehicle.

[0069] Optionally, the electric vehicle scheduled charging intelligent control system also includes: The data acquisition and correlation module is used to obtain the historical charge-discharge cycle results of electric vehicles, and to obtain the battery initial state data, charging process data and battery health state degradation amount for each charging cycle from the historical charge-discharge cycle results. The model training set generation module is used to associate and match the initial state data of the battery in each charging cycle with the charging process data of the corresponding charging cycle based on the battery health state decay, so as to obtain the model training dataset. The model training and generation module is used to train the initial prediction model based on the training dataset to obtain the battery health prediction model.

[0070] Optional, data acquisition module, specifically including: The vehicle data request submodule is used to obtain the vehicle identity information and time target of the target vehicle from the target charging request, and send a data request to the battery management system of the target vehicle based on the vehicle identity information to obtain battery health status data. The market data acquisition submodule is used to acquire electricity market price data within the time window corresponding to the time target through the electricity trading market platform.

[0071] Optional, the constraint determination module specifically includes: The safety status parameter acquisition submodule is used to acquire the battery safety status parameters of the target vehicle based on the battery health status data. The battery safety status parameters include the real-time battery temperature, battery internal resistance, and state of charge. The candidate scheme evaluation submodule is used to obtain multiple preset candidate charging power values, combine the battery safety state parameters with multiple candidate charging power values, and input each combination into the battery health prediction model to obtain multiple corresponding expected battery health state degradation amounts. The optimal power selection submodule is used to compare the expected battery health state degradation with the preset loss threshold, select the optimal candidate charging power value that does not exceed the preset loss threshold from the comparison results, and use the optimal candidate charging power value as the upper limit constraint value of charging power.

[0072] Optional, the target optimization module specifically includes: The priority sequence generation submodule is used to divide the time window into multiple discrete time units, and based on electricity market price data, sort the multiple time units according to the charging cost order to generate a priority charging sequence. The power allocation and curve synthesis submodule is used to allocate charging power to time units sequentially according to the priority charging sequence, so that the charging power does not exceed the upper limit constraint value of the charging power, until the total allocated charging amount meets the total charging amount target in the target charging request, and generates a charging power curve based on each time unit and the corresponding charging power.

[0073] Optional, the instruction control module specifically includes: The instruction issuing submodule is used to generate charging instructions based on the charging power curve, according to the time unit and corresponding charging power in each charging power curve, and to charge the target vehicle according to the charging instructions.

[0074] Optionally, the electric vehicle scheduled charging intelligent control system also includes: An alternative power setting module is used to set the charging power upper limit constraint value as the charging power setting value for the time unit. The alternative strategy generation module is used to calculate the charging amount based on the charging power setting value for each time unit in chronological order until the cumulative charging amount meets the total charging amount target. Based on each time unit and the corresponding charging power setting value, an alternative charging power curve is generated. The cost-benefit estimation module is used to calculate the corresponding estimated total charging time and estimated total charging cost based on electricity market price data, according to the charging power curve and the alternative charging power curve respectively. The user interaction decision module is used to send the estimated total charging time and estimated total charging cost to the user terminal, and to receive selection instructions from the user terminal. The selection instructions are used to determine the charging power curve required by the user.

[0075] Specific limitations regarding the intelligent control system for scheduled charging of electric vehicles can be found in the limitations of the intelligent control method for scheduled charging of electric vehicles mentioned above, and will not be repeated here. Each module in the aforementioned intelligent control system for scheduled charging of electric vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in the processor of the electronic device in hardware form or independently of the processor, or stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0076] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, the electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data such as battery health status, electricity market price data, charging power upper limit constraints, and battery health prediction models. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a smart control method for scheduled charging of electric vehicles.

[0077] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: In response to a user's target charging request, the system obtains the target vehicle's battery health status data and electricity market price data based on the target charging request. Determine the upper limit constraint value of charging power based on battery health status data; Based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a charging power curve is generated; Based on the charging power curve, a charging command is generated, which is used to control the charging station to charge the target vehicle.

[0078] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: In response to a user's target charging request, the system obtains the target vehicle's battery health status data and electricity market price data based on the target charging request. Determine the upper limit constraint value of charging power based on battery health status data; Based on the upper limit constraint value of charging power, combined with the target charging request and electricity market price data, a charging power curve is generated; Based on the charging power curve, a charging command is generated, which is used to control the charging station to charge the target vehicle.

[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0081] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A smart control method for scheduled charging of electric vehicles, characterized in that, The intelligent control method for scheduled charging of electric vehicles includes: In response to a user-initiated target charging request, the system acquires battery health status data and electricity market price data of the target vehicle based on the target charging request. Based on the battery health status data, determine the upper limit constraint value of charging power; Based on the charging power upper limit constraint value, combined with the target charging request and the electricity market price data, a charging power curve is generated; Based on the charging power curve, a charging command is generated, which is used to control the charging pile to charge the target vehicle.

2. The intelligent control method for scheduled charging of electric vehicles according to claim 1, characterized in that, The intelligent control method for scheduled charging of electric vehicles also includes: Obtain the historical charge-discharge cycle results of the electric vehicle, and extract the battery initial state data, charging process data, and battery health state degradation amount for each charging cycle from the historical charge-discharge cycle results; Based on the battery health state decay, the battery initial state data of each charging cycle is associated and matched with the charging process data of the corresponding charging cycle to obtain the model training dataset. The initial prediction model is trained based on the training dataset to obtain the battery health prediction model.

3. The intelligent control method for scheduled charging of electric vehicles according to claim 1, characterized in that, The step of obtaining battery health status data and electricity market price data of the target vehicle based on the target charging request specifically includes: Obtain the vehicle identity information and time target of the target vehicle from the target charging request, and send a data request to the battery management system of the target vehicle based on the vehicle identity information to obtain the battery health status data. Based on the stated time objective, electricity market price data within the corresponding time window is obtained through the electricity trading market platform.

4. The intelligent control method for scheduled charging of electric vehicles according to claim 2, characterized in that, The step of determining the upper limit constraint value of charging power based on the battery health status data specifically includes: Based on the battery health status data, the battery safety status parameters of the target vehicle are obtained, including the real-time battery temperature, battery internal resistance, and current remaining charge. Multiple preset candidate charging power values ​​are obtained, the battery safety state parameter is combined with the multiple candidate charging power values, and each combination is input into the battery health prediction model to obtain multiple corresponding expected battery health state degradation amounts. The expected battery health degradation is compared with a preset loss threshold. The optimal candidate charging power value that does not exceed the preset loss threshold is selected from the comparison results, and the optimal candidate charging power value is used as the upper limit constraint value of the charging power.

5. The intelligent control method for scheduled charging of electric vehicles according to claim 3, characterized in that, The process of generating a charging power curve based on the charging power upper limit constraint, combined with the target charging request and the electricity market price data, specifically includes: The time window is divided into multiple discrete time units. Based on the electricity market price data, the multiple time units are sorted according to the charging cost order to generate a priority charging sequence. According to the priority charging sequence, charging power is allocated to the time units in sequence so that the charging power does not exceed the upper limit constraint value of the charging power, until the total amount of charging allocated meets the total amount of charging target in the target charging request. The charging power curve is generated according to each time unit and the corresponding charging power.

6. The intelligent control method for scheduled charging of electric vehicles according to claim 5, characterized in that, Based on the charging power curve, a charging command is generated. This charging command is used to control the charging pile to charge the target vehicle, specifically including: Based on the charging power curve, a charging instruction is generated according to the time unit and corresponding charging power in each charging power curve, and the target vehicle is charged according to the charging instruction.

7. The intelligent control method for scheduled charging of electric vehicles according to claim 5, characterized in that, The intelligent control method for scheduled charging of electric vehicles also includes: The upper limit constraint value of the charging power is used as the charging power setting value of the time unit; Calculate the charging amount for each time unit based on the charging power setting value in chronological order until the cumulative charging amount meets the total charging amount target. Generate alternative charging power curves based on each time unit and the corresponding charging power setting value. Based on the electricity market price data, the estimated total charging time and estimated total charging cost are calculated according to the charging power curve and the alternative charging power curve, respectively. The estimated total charging time and estimated total charging cost are sent to the user terminal, and a selection instruction is received from the user terminal. The selection instruction is used to determine the charging power curve required by the user.

8. An intelligent control system for scheduled charging of electric vehicles, characterized in that, The intelligent control system for scheduled charging of electric vehicles includes: The data acquisition module is used to respond to a user-initiated target charging request and, based on the target charging request, acquire battery health status data and electricity market price data of the target vehicle. The safety constraint determination module is used to determine the upper limit constraint value of charging power based on the battery health status data; The multi-objective optimization and curve generation module is used to generate a charging power curve based on the charging power upper limit constraint value, combined with the target charging request and the electricity market price data; The instruction generation and execution control module is used to generate charging instructions based on the charging power curve, and the charging instructions are used to control the charging pile to charge the target vehicle.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the electric vehicle scheduled charging intelligent control method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the electric vehicle scheduled charging intelligent control method as described in any one of claims 1 to 7.

Images