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CN-122022232-A - Alternating-current ordered charging demand acquisition method, equipment, storage medium and product

CN122022232ACN 122022232 ACN122022232 ACN 122022232ACN-122022232-A

Abstract

The invention discloses a method, equipment, a storage medium and a product for acquiring alternating-current ordered charging requirements, relates to the field of ordered charging of new energy automobiles, and realizes accurate balance of charging requirements and pile network loads through digital interaction and intelligent analysis of piles. The method comprises the steps of establishing communication through a specific duty ratio PWM signal after a charging pile is connected with a vehicle-mounted charger, synchronously collecting static parameters of an electric vehicle, operation data of the charging pile and user interaction data, constructing a charging behavior feature vector integrating multi-dimensional features through outlier rejection and normalization processing, dividing a scene into three types of high, medium and low priorities by adopting a K-means algorithm, introducing correction factors to construct a differentiated charging power and duration model, and pushing an integrated demand list to a management platform to dynamically update according to a fixed period. The method solves the problems of poor pile adaptation, one-sided data, insufficient scene adaptation and the like in the prior art, and improves communication compatibility and data accuracy.

Inventors

  • YUE HUSHENG
  • LUO ZHENCHAO
  • LIU YUN
  • AO LEILEI
  • CAI YUN
  • LIU MENGFEI
  • JIN XIA
  • DU ZHENCHUAN

Assignees

  • 国网江西省电力有限公司南昌供电分公司

Dates

Publication Date
20260512
Application Date
20251215

Claims (10)

  1. 1. The alternating current ordered charging demand acquisition method is characterized by comprising the following steps of: the method comprises the following steps that S1, after a charging pile is connected with a vehicle-mounted charger, a digital communication request is initiated through a PWM signal with a specific duty ratio, and the vehicle-mounted charger is required to complete switching of switch states meeting interval requirements for more than 5 times within 100 milliseconds and keep off so as to respond to the communication requirement; S2, after the charging pile is identified and responded, establishing connection, sending a data acquisition instruction to the vehicle-mounted charger through a serial port data format to acquire static parameters of the electric vehicle, and synchronously collecting operation data of the charging pile and user interaction data; S3, carrying out outlier rejection and normalization processing on the original data, and constructing a user charging behavior feature vector based on the processed data; S4, based on the user charging behavior feature vector, dynamically classifying charging scenes by adopting a K-means algorithm, and classifying the charging scenes into three categories of high priority, medium priority and low priority by determining scene adaptation coefficients; S5, setting differential targets aiming at three different priorities, introducing a charging demand correction factor, and respectively constructing a charging power and duration demand model; And S6, integrating the charging power requirement, the charging duration requirement and the scene priority information into an ordered charging requirement list, pushing the ordered charging requirement list to a charging management platform in real time, updating the ordered charging requirement list according to a fixed period, and periodically repeating the whole process to realize dynamic regulation of the requirement.
  2. 2. The method for acquiring alternating current ordered charging requirements according to claim 1, wherein after the charging pile and the vehicle-mounted charger are connected, step S1 is characterized in that a PWM signal with 5% duty ratio is sent on a CP circuit, the duration is 200 ms, then 100% duty ratio output is kept, namely, high level is continuously output, the digital communication is needed, if the vehicle-mounted charger supports the function, the switching of the on-off state of the vehicle-mounted charger is needed to be completed within 100 ms, the state switching interval is not less than 1 ms, the off state is kept after the completion, and the charging pile enters a standard charging flow without digital communication if the switching of the on-off state of the vehicle-mounted charger is not detected within 100 ms after the 5% signal is sent; After the charging pile recognizes the response of the vehicle-mounted charger, a data acquisition instruction is sent to the vehicle-mounted charger through a serial port data format, namely a 1-bit start bit, an 8-bit data bit and a 1-bit stop bit, wherein the instruction content comprises an instruction code, an instruction length, instruction data and a check code, and the output high level is kept after the transmission is completed; The vehicle-mounted charger receives the instruction and then sends failure information when decoding and checking fail, and if successful, sending the data required by the instruction.
  3. 3. The alternating current ordered charging demand acquisition method according to claim 2 is characterized in that after data transmission is completed, a vehicle-mounted charger keeps a switch-off state of the vehicle-mounted charger, waits for detecting whether information interaction is finished, a charging pile enters a next stage after decoding return information of the vehicle-mounted charger successfully, a command flow is retried after failure, the retried times are 3 at maximum, after data reading is completed, the charging pile switches a CP voltage more than 5 times within 100 milliseconds after the charging demand information is acquired, a voltage state switching interval is not less than 5 milliseconds each time, a high level is kept after completion, and then a standard charging flow is entered.
  4. 4. The method for acquiring alternating-current ordered charging requirements according to claim 1, wherein step S2 is specifically to establish connection after charging pile identification response, send a data acquisition instruction to a vehicle-mounted charger through a serial port data format to acquire static parameters of an electric vehicle, and synchronously collect charging pile operation data and user interaction data, wherein the charging pile operation data comprises real-time line voltage Power factor Node load factor The static parameters of the electric automobile comprise the rated capacity of the battery Current residual capacity Health of battery Rated current of charging interface The user interaction data comprises a user reservation charging starting period Desired charge end period Minimum expected remaining capacity 。
  5. 5. The method for acquiring alternating-current ordered charging requirements according to claim 1, wherein step S3 is specifically performed on the raw data by outlier rejection and normalization, and a user charging behavior feature vector is constructed based on the processed data, and the expression of the feature vector V is as follows: ; In the formula, Charging behavior feature vectors for a user; is all characteristic weight coefficient, the sum of which satisfies The specific value is determined as by combining analytic hierarchy process with entropy weight process ; The current residual electric quantity of the electric automobile; The maximum electric quantity of the battery of the electric automobile; a charge end period desired for a user; a charging start period reserved for a user; Setting the standard charging time to 8 hours; The battery health degree of the electric automobile is within the range of 0 and 1; Rated current of a charging interface of the electric automobile; maximum charging current allowed for the charging pile; The real-time load rate of the charging pile is within the range of 0 and 1.
  6. 6. The method for acquiring the alternating-current ordered charging demand according to claim 1, wherein step S4 is specifically based on a user charging behavior feature vector, a K-means algorithm is adopted to dynamically classify charging scenes, a scene adaptation coefficient λ is determined, when λ is greater than or equal to 0.75, a high-priority scene is determined, when λ is greater than or equal to 0.4 and less than or equal to 0.75, a medium-priority scene is determined, and when λ is less than or equal to 0.4, a low-priority scene is determined, wherein the calculation formula of the scene adaptation coefficient λ is as follows: ; In the formula, The value range of the scene adaptation coefficient is between 0 and 1; The number of the clustering centers is a fixed value, and the number of the clustering centers is 3 and is set to be uniform corresponding to three types of high, medium and low charging scenes without units; For Euclidean distance, for calculating user charging behavior feature vector And the first Class scene clustering center vector Similarity between; is the first Clustering center vectors of the class scene are obtained through clustering analysis of a K-means algorithm; the bandwidth of the kernel function is a fixed value and is uniformly set to 0.15; is the first Charging pile bearing coefficient of class scene, wherein high priority scene corresponds to Medium priority scene correspondence Low priority scene correspondence 。
  7. 7. The method of claim 1, wherein step S5 sets a differentiation target for three types of different priorities, introduces a charging demand correction factor, and constructs a charging power and duration demand model respectively, specifically includes: For a high priority scenario, targeting user demand priority and charging pile safety constraints, charging power demand Charging duration requirement The calculation formula of (2) is as follows: ; ; In the formula, Real-time charging power requirements in a high priority scenario; Real-time line voltage of the charging pile; the method comprises the steps of (1) charging an interface rated current for an electric automobile; the real-time power factor of the charging pile is in the range of 0 and 1; allowing a maximum charging current for the charging pile; the real-time load rate of the charging pile is calculated; the value range of the charge pile load influence factor is between 0.5 and 1, and the calculation formula is ; The charging duration requirement is met in a high-priority scene; The rated capacity of the battery of the electric automobile; The minimum expected residual capacity set for the user, namely the minimum electric quantity standard which is required to be reached after the user is charged; The current residual electric quantity of the electric automobile; real-time charging power requirements in a high priority scenario; for the correction coefficient of the charging efficiency, the value range is between [0.92,0.95], and the calculation formula is ; For a medium priority scene, taking charge pile load balance and user demand as targets and charging power demand Charging duration requirement The calculation formula of (2) is as follows: ; ; In the formula, Real-time charging power requirements in a medium priority scenario; real-time line voltage for the charging pile; allowing a maximum charging current for the charging pile; Is the circumference ratio; Is real-time; Reserving a charging start period for a user; A charging end period is desired for the user; real-time power factor for the charging pile; the charging duration requirement under the medium priority scene is met; is the rated capacity of the battery of the electric automobile, A minimum expected residual capacity for the user, Is the current residual electric quantity of the electric automobile, Correcting the coefficient for the charging efficiency; Real-time charging power requirements in a medium priority scenario; The elastic time length correction is calculated by the following formula ; For low priority scenarios, charging power demand is targeted at charging pile load optimization Charging duration requirement The calculation formula of (2) is as follows: ; ; In the formula, Real-time charging power requirements in low priority scenarios; real-time line voltage of the charging pile, Allow maximum charging current for the charging pile, The real-time load rate of the charging pile, Real-time power factor for the charging pile; The daily average load rate of the charging pile is in the range of 0, 1; The charging duration requirement is met in a low-priority scene; is the rated capacity of the battery of the electric automobile, A minimum expected residual capacity for the user, Is the current residual electric quantity of the electric automobile, Real-time charging power requirements for low priority scenarios, Correction coefficient for charging efficiency, Is an elastic duration correction amount; is double the resilience duration of the low priority scene.
  8. 8. A computer device comprising at least one processor and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor, which when executed by the at least one processor, cause the at least one processor to perform the method of any of claims 1 to 7.
  9. 9. A non-transitory computer-readable storage medium storing computer instructions which, when executed by at least one processor, cause the at least one processor to perform the method of any one of claims 1 to 7.
  10. 10. A computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1 to 7.

Description

Alternating-current ordered charging demand acquisition method, equipment, storage medium and product Technical Field The invention relates to the field of ordered charging of new energy automobiles, in particular to a method, equipment, a storage medium and a product for acquiring alternating current ordered charging requirements. Background With the global energy structure transformation and the 'double carbon' target promotion, new energy electric automobile industry is exploded, the amount of energy is continuously increased, and the charging infrastructure construction also enters a large-scale expansion stage. However, the contradiction between the centralized burst of the charging demand and the limited power grid admittance capability is increasingly highlighted, the disordered charging behavior has become a key bottleneck restricting the high-quality development of industry, the communication between the current alternating-current charging pile and the electric automobile is realized through CC/CP, wherein CC is a connection confirmation function, the reliable connection between the charging plug and the socket is ensured, the maximum current carrying capability of the cable is determined according to the resistance, CP is a control guiding function, the states of the power supply device and the vehicle controller are determined according to the CP voltage, the maximum current which can be provided by the power supply device is appointed by the PWM duty ratio of the CP in the energy transmission process, and the vehicle controller regulates the charging current according to the information so as not to exceed the power supply capability of the power supply device. The alternating-current charging lacks information interaction of charging requirements, a large number of electric vehicles are intensively charged in a peak period, the problems of local power grid load overload, voltage drop, power factor unbalance and the like are easily caused, the safe and stable operation of a power grid is influenced, chain reactions such as charging efficiency reduction and equipment loss aggravation and the like are possibly caused, charging resources are idle in a valley period, power resource waste is caused, and the space-time mismatch contradiction between charging supply and demand is further amplified. The traditional scheme adopts fixed priority division or simple rule judgment, does not construct a multi-dimensional characteristic system of a fusion vehicle, a pile and a person, and cannot dynamically adapt to charging scenes and power grid running states of different users. The situation classification lacks quantitative index support, so that the charging scheme is insufficient in pertinence, and the user demands and the power grid load optimization targets are difficult to balance. The existing charging strategy does not formulate a differential target according to scene priority, so that emergency charging experience of high-demand users cannot be guaranteed, and peak clipping and valley filling of a power grid are difficult to realize through dynamic power adjustment. More critical, most schemes are static generation, lack of a real-time updating mechanism, and cannot respond to dynamic factors such as load fluctuation of a charging pile, battery state change and the like, so that charging efficiency and power grid operation efficiency are difficult to consider. In summary, the existing alternating-current charging demand acquisition technology has significant shortcomings in the aspects of communication suitability, data acquisition comprehensiveness, scene classification intellectualization, scheme differentiation, dynamic adjustment capability and the like, and is difficult to meet multiparty balance demands in a large-scale charging scene. Therefore, research and development of an alternating current ordered charging demand acquisition method with high compatibility, accurate data support, intelligent scene adaptation and dynamic adjustment capability has become an urgent technical demand in the field of new energy automobile charging. Disclosure of Invention The invention aims to provide an alternating current ordered charging demand acquisition method, equipment, a storage medium and a product, wherein digital communication is established through specific duty ratio PWM signals of a charging pile and a vehicle-mounted charger, static parameters of an electric vehicle, charging pile operation data and user interaction data are collected, eigenvectors are constructed through outlier rejection and normalization processing, a charging scene is divided into three types of high, medium and low priorities by adopting a K-means algorithm, a differentiated charging power and duration model is constructed according to different priorities by introducing correction factors, and finally a requirement list is integrated and pushed to a charging management platform and is updated periodically and dynamical