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CN-122008949-A - Charging control method and system for electric automobile in power distribution network

CN122008949ACN 122008949 ACN122008949 ACN 122008949ACN-122008949-A

Abstract

The embodiment of the application discloses a charging control method and a charging control system for an electric automobile in a power distribution network, wherein the system comprises a local control unit and a cloud optimization unit, the method comprises the steps that the local control unit obtains power distribution network data, fuzzy charging demand data of a user, battery state and complex impedance data, analyzes the battery aging state based on the complex impedance data, determines real-time health factors, further calculates battery loss cost during charging, and generates a schedulable feature vector representing schedulable capacity of the electric automobile based on the battery loss cost and various data obtained by the local control unit. And solving to obtain cluster scheduling prices of each class cluster by taking the minimized system objective function as a target. And the local control unit makes a charge and discharge plan through a decision mechanism according to the cluster dispatching price and the dispatching feature vector, and executes the charge and discharge plan according to the battery state and the communication state with the cloud.

Inventors

  • LIN GUANWU

Assignees

  • 兰州交通大学

Dates

Publication Date
20260512
Application Date
20260318

Claims (10)

  1. 1. The charging control method for the electric automobile in the power distribution network is characterized by being applied to a charging control system of the electric automobile in the power distribution network, wherein the system comprises a local control unit and a cloud optimization unit, and the method comprises the following steps: acquiring power distribution network data, fuzzy charging demand data of a user of a current electric automobile, current state data of a battery and complex impedance data of the battery by using the local control unit; The method comprises the steps of obtaining battery aging data of a current electric automobile based on battery complex impedance data by using a local control unit, determining real-time health factors of the current battery based on the battery aging data, calculating battery loss cost through a pre-constructed battery aging cost model based on the real-time health factors, and obtaining a schedulable feature vector of the current electric automobile based on the battery loss cost, the fuzzy charging demand data of a user and the current state data of the battery; Based on the category clusters and the schedulable feature vectors, taking a value of a target optimization function which is constructed in advance as an optimization target, solving the target optimization function to obtain a cluster scheduling price corresponding to the category clusters; The local control unit is utilized to receive the cluster scheduling price, and a charging and discharging plan of the current electric automobile is determined through a preset decision mechanism based on the cluster scheduling price and the schedulable feature vector; And executing the charge and discharge plan based on the current state data of the battery and the communication state of the local control unit and the cloud optimization unit by using the local control unit.
  2. 2. The method of claim 1, wherein the determining a real-time health factor for a current battery based on the battery aging data comprises: Acquiring a charge transfer impedance standard value and an optimal working temperature of the current battery by using the local control unit; obtaining battery aging data of the current battery by fitting a pre-constructed equivalent circuit model based on the battery complex impedance data by using the local control unit, wherein the battery aging data comprises current charge transfer impedance and current temperature; calculating an impedance aging term of the current charge transfer impedance deviating from the charge transfer impedance standard value by using the local control unit; calculating a temperature deviation term of the current temperature from the optimal working temperature of the battery by using the local control unit; And utilizing the local control unit to perform fusion calculation on the impedance aging item and the temperature deviation item through a nonlinear amplification mechanism, so as to obtain the real-time health factor which generates an exponential penalty response when the impedance is abnormally increased or the temperature is extremely deviated.
  3. 3. The method according to claim 1, wherein the method further comprises: Multiplexing a direct current-direct current converter built in the local control unit into a signal generator by using the local control unit when the current battery and the local control unit are in charging connection, and superposing sinusoidal alternating current signals containing a plurality of different frequency components on direct current charging current and applying the sinusoidal alternating current signals to the current battery by carrying out perturbation modulation on Pulse Width Modulation (PWM) signals of a power switch tube in the direct current-direct current converter; Measuring response data of the current battery to each frequency component by using the local control unit to obtain a corresponding voltage response signal and a corresponding current response signal; denoising the voltage response signal and the current response signal based on a discrete wavelet transform algorithm by using the local control unit to obtain a denoised voltage response signal and a denoised current response signal; and calculating a complex impedance value corresponding to each frequency component by using the local control unit based on the denoised voltage response signal and the denoised current response signal to obtain the complex impedance data of the battery.
  4. 4. The method according to claim 1, wherein the method further comprises: constructing an initial battery aging model frame; Acquiring historical battery aging data and historical health factors; the historical battery aging data and the historical health factors are brought into the initial battery aging model framework to obtain battery aging parameters; and determining the pre-constructed battery aging cost model based on the battery aging parameters.
  5. 5. The method of claim 1, wherein the schedulable feature vector includes an urgency index, a flexibility index, a health confidence, and a capacity contribution value, wherein the deriving the schedulable feature vector for the current electric vehicle based on the battery depletion cost, the user fuzzy charge demand data, and the battery current state data includes: Obtaining rated power corresponding to the local control unit; Based on the fuzzy charging demand data of the user and the current state data of the battery, respectively obtaining the urgency index and the flexibility index; Determining the health confidence level characterizing a willingness of the battery to participate in discharging based on the battery loss cost; Obtaining a capacity contribution value based on the rated power and the current state data of the battery by using the local control unit; and utilizing the local control unit to aggregate and generate the schedulable feature vector of the current electric automobile based on the urgency index, the flexibility index, the health confidence and the capacity contribution value.
  6. 6. The method of claim 1, wherein the determining, by the cloud optimization unit, a class cluster corresponding to the current electric vehicle based on the schedulable feature vector and a preset classification rule, and solving, by using a value of a target optimization function that is minimized and constructed in advance as an optimization target based on the class cluster and the schedulable feature vector, the target optimization function to obtain a cluster scheduling price corresponding to the class cluster, comprises: calculating the space distance between the schedulable feature vector and the center vector of each preset class cluster by using the cloud optimization unit; Determining a class cluster corresponding to the current electric automobile based on the space distance by utilizing the cloud optimization unit; determining average urgency corresponding to the category cluster based on the schedulable feature vector by using the cloud optimization unit; Acquiring real-time cost electricity price of the power distribution network by utilizing the cloud optimization unit; And inputting the real-time cost electricity price and the average urgency into the pre-built target optimization function by using the cloud optimization unit, and solving the target optimization function by taking a value of the minimized pre-built target optimization function as an optimization target based on a horse group optimization algorithm to obtain a cluster scheduling price corresponding to the class cluster and the electricity purchasing power of the power distribution network.
  7. 7. The method of claim 1, wherein the schedulable feature vector includes an urgency index, wherein the charge-discharge plan includes a first charge-discharge plan and a second charge-discharge plan, wherein the receiving, with the local control unit, the clustered scheduling price, determining, based on the clustered scheduling price and the schedulable feature vector, a charge-discharge plan for the current electric vehicle through a preset decision mechanism, comprises: When the local control unit receives the cluster scheduling price, comparing the cluster scheduling price with the urgency index and the battery loss cost respectively; When the urgency index is larger than the cluster scheduling price, determining that the rigidity requirement is dominant, and determining the first charge-discharge plan which ignores the economic cost and charges with the maximum allowable power by using the local control unit; And when the urgency index is smaller than or equal to the cluster scheduling price, determining that economy is dominant, and determining the second charge-discharge plan for responding to the regulating signal to reduce the charge power, wherein the charge priority of the second charge-discharge plan is higher than that of the first charge-discharge plan.
  8. 8. The method of claim 1, wherein the battery current state data comprises a battery temperature, wherein the executing, with the local control unit, the charge and discharge schedule based on the battery current state data and a communication state of the local control unit and the cloud optimization unit comprises: when the battery temperature is lower than a preset temperature threshold value, distributing heating power and charging power of the current battery based on a preset battery thermal model by using the local control unit so as to preheat the current battery; And executing the charge and discharge plan based on the communication state of the local control unit and the cloud optimization unit after the preheating of the current battery is completed by utilizing the local control unit.
  9. 9. The method of claim 8, wherein the executing the charge-discharge plan based on the communication status of the local control unit and the cloud optimization unit comprises: executing the charge-discharge plan by using the local control unit when communication is normal; When communication is interrupted, the local control unit is utilized to determine an updated charge-discharge plan based on the power distribution network data, wherein the updated charge-discharge plan comprises the steps of reducing the charge power corresponding to the charge-discharge plan and keeping the charge power corresponding to the charge-discharge plan unchanged; when communication is recovered, after the preset communication quality condition is met, the local control unit is accessed to the cloud optimization unit.
  10. 10. The charging control system of the electric automobile in the power distribution network is characterized by comprising a local control unit and a cloud optimization unit; The local control unit is used for acquiring power distribution network data, fuzzy charging demand data of a user of the current electric automobile, current state data of a battery and complex impedance data of the battery; The local control unit is also used for obtaining battery aging data of the current electric automobile based on the battery complex impedance data, determining a real-time health factor of the current battery based on the battery aging data, calculating battery loss cost through a pre-constructed battery aging cost model based on the real-time health factor, and obtaining a schedulable feature vector of the current electric automobile based on the battery loss cost, the fuzzy charging demand data of the user and the current state data of the battery; The cloud optimization unit is used for determining a class cluster corresponding to the current electric automobile through a preset classification rule based on the schedulable feature vector, solving a target optimization function based on the class cluster and the schedulable feature vector by taking a value of a minimum pre-constructed target optimization function as an optimization target, and obtaining a cluster scheduling price corresponding to the class cluster; the local control unit is used for receiving the cluster scheduling price, and determining a charging and discharging plan of the current electric automobile through a preset decision mechanism based on the cluster scheduling price and the schedulable feature vector; The local control unit is used for executing the charge and discharge plan based on the current state data of the battery and the communication state of the local control unit and the cloud optimization unit.

Description

Charging control method and system for electric automobile in power distribution network Technical Field The application relates to the field of collaborative optimization control of electric vehicles and power distribution networks, in particular to a charging control method and a charging control system for electric vehicles in a power distribution network. Background With the advancement of global carbon peak and carbon neutralization, the integration of energy internet and traffic electrification has become a necessary trend. Electric vehicles are evolving from pure vehicles to distributed mobile energy storage units as core carriers. The holding amount of the electric automobile is expected to exponentially increase, and the electric automobile load with mass, dispersion and high randomness is accessed, so that the planning and operation of the traditional distribution network form unprecedented pressure. Traditional distribution network design relies on load prediction based on statistical macroscopic rules, and load characteristics are relatively stable. However, the charging load of electric vehicles shows significant space-time uncertainty, and particularly in large residential communities, the peak of residential electricity consumption in late peak hours and the peak of centralized charging of private vehicles are very easy to be overlapped. If the double peak superposition phenomenon is not effectively guided, the instantaneous increase of the load rate of the distribution transformer can be caused, so that equipment is damaged, and the electric energy quality is deteriorated. In the related art, based on a price-induced demand response technology, a user is guided to spontaneously charge off-peak through economic levers such as time-of-use electricity price and the like. The centralized optimization control technology based on the model depends on a cloud computing center, and a scheduling instruction is issued to each charging pile by acquiring accurate data such as the battery real-time state of the whole-network electric vehicle and the travel planning of a user and solving a global optimization model. Such centralized architectures are highly dependent on a constantly stable communication link to maintain a closed control loop. However, the battery aging model on which the centralized optimization control technology depends is generally static and linear, and cannot accurately reflect the nonlinear aging characteristic of the complex electrochemical process of the lithium ion battery in actual operation, so that a scheduling instruction may accelerate battery damage or cause energy storage resource waste. The cloud computing center collects sensitive data such as accurate travel of the user, and the user privacy is exposed to leakage risks. With the increase of the scale of the accessed electric automobile, the problem of solving the high-dimensional optimization by the cloud end faces a computational bottleneck, and the real-time scheduling requirement of the power distribution network is difficult to meet. Because the control logic of the whole system is seriously dependent on a cloud computing center instruction, the local autonomous decision-making capability is lacking, and when communication is interrupted due to base station faults or network attacks, no physical defense mechanism at the equipment level exists, and local power distribution network breakdown can be caused by control failure. The optimization objective of the existing strategy generally ignores the influence of physical environment, and particularly balances the charge safety and efficiency in a low-temperature environment. Disclosure of Invention In view of the above, the embodiment of the application provides a charging control method and a charging control system for an electric vehicle in a power distribution network, which at least solve the problems that a battery of the electric vehicle is damaged due to a charging control instruction and privacy is revealed due to the fact that a cloud computing center collects user sensitive numbers. The technical scheme of the embodiment of the application is realized as follows: in a first aspect, an embodiment of the present application provides a charging control method for an electric vehicle in a power distribution network, which is applied to a charging control system for the electric vehicle in the power distribution network, where the system includes a local control unit and a cloud optimization unit, and the method includes: acquiring power distribution network data, fuzzy charging demand data of a user of a current electric automobile, current state data of a battery and complex impedance data of the battery by using the local control unit; The method comprises the steps of obtaining battery aging data of a current electric automobile based on battery complex impedance data by using a local control unit, determining real-time health factors of the current battery ba