Method for optimizing charging efficiency of charging pile and related device

Abstract

This application proposes a method and related apparatus for optimizing the charging efficiency of charging piles. The method includes: acquiring multiple power parameters for the current cycle through the controller of a charging efficiency optimization system; determining multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; determining multiple second charging efficiency parameters for a first step-length search phase; if the second charging efficiency parameters meet the flow conditions of the second step-length search phase, determining multiple golden section efficiency points and multiple first section point efficiency parameters for the second step-length search phase based on the multiple second charging efficiency parameters; determining convergence efficiency parameters based on the multiple golden section efficiency points and multiple first section point efficiency parameters; and determining the power parameters for the next cycle based on the convergence efficiency parameters and performing tracking and control. Thus, under fixed demand, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the efficiency range limitations of charging piles, enabling the equipment to continuously operate within the maximum efficiency range, and reducing overall losses.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, and in particular to a method and related apparatus for optimizing the charging efficiency of charging piles. Background Technology

[0002] With the continuous increase in the number of new energy vehicles, the industry is currently focusing on "power race" and "density improvement." For example, the industry is using megawatt-level supercharging to improve charging speed and network density. However, this has not fundamentally solved the problem of a narrow efficiency range: the peak efficiency of charging piles is only achieved within a narrow voltage / power range, while the actual charging range for users is much wider, resulting in a significant decrease in average efficiency; thermal management energy consumption surges during high-power charging; to ensure high compatibility, operators need to configure modules with a wide voltage range, but the efficiency of traditional topologies drops sharply under non-standard operating conditions, causing energy waste on the grid side and higher electricity prices for users. In summary, under complex operating conditions with a wide power range, varying ambient temperature, and multiple voltage requirements, there are still problems such as insufficient overall efficiency of the charging system, unstable high-power output, and high overall losses. Summary of the Invention

[0003] This application provides a method and related apparatus for optimizing the charging efficiency of a charging pile, which can break through the efficiency range limitation of the charging pile, enable the equipment to continuously operate in the maximum efficiency range, and reduce overall losses.

[0004] In a first aspect, embodiments of this application provide a method for optimizing the charging efficiency of a charging pile, applied to a controller of a charging efficiency optimization system, wherein the charging efficiency optimization system further includes multiple target charging piles, and the method includes:

[0005] Acquire multiple power parameters for the current cycle, and determine multiple first charging efficiency parameters based on the multiple power parameters for the current cycle;

[0006] Based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter, multiple second charging efficiency parameters are determined for the first long search phase.

[0007] If the second charging efficiency parameter meets the flow conditions of the second step size search stage, multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step size search stage are determined based on the multiple second charging efficiency parameters.

[0008] Based on the multiple golden section efficiency points and multiple first section point efficiency parameters, iterative calculation operations are performed to determine the convergence efficiency parameters.

[0009] Based on the convergence efficiency parameters, the power parameters for the next cycle are determined and tracked and adjusted to maximize the operating efficiency of the charging pile in the next cycle.

[0010] In one possible embodiment, a plurality of current charging efficiencies are determined based on the plurality of current input power parameters and the plurality of current output power parameters;

[0011] The power grid frequency period is obtained, which is the operating frequency of the power grid where the target charging pile is located.

[0012] The attenuation weighting factor is determined based on the power frequency cycle of the power grid.

[0013] The plurality of first charging efficiency parameters are determined based on the attenuation weighting factor and the plurality of current charging efficiencies, and the plurality of first charging efficiency parameters are associated with the plurality of current input power parameters and / or the plurality of current output power parameters.

[0014] In one possible embodiment, determining multiple second charging efficiency parameters for the first long search phase based on the plurality of power parameters, the first charging efficiency parameter, the plurality of efficiency thresholds, and the first power parameter includes:

[0015] The first step length is determined based on the first power parameter, and the first search power range is determined based on the plurality of power parameters;

[0016] A first search operation is performed based on the first step length, the first search power range, and initialization information, wherein the initialization information includes the initial power parameter.

[0017] The first search operation includes: searching within the first search power range based on the starting power and the first step length to determine the sampling power parameter and the sampling efficiency parameter of the current sampling point, wherein the sampling power parameter of the current sampling point is one of the plurality of power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the plurality of second charging efficiency parameters.

[0018] In one possible embodiment, the second charging efficiency parameter includes at least one of the sampling efficiency parameter of the next sampling point and the sampling efficiency parameter of the current sampling point. The transition conditions of the second step search phase include: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or the difference between the sampling efficiency parameter of the current sampling point and the sampling efficiency parameter of the next sampling point is greater than a preset efficiency decrease threshold.

[0019] In one possible embodiment, determining the multiple golden section efficiency points and multiple first segmentation point efficiency parameters in the second step size search stage based on the multiple second charging efficiency parameters includes:

[0020] The second step size is determined based on the first power parameter, and the second search power interval is determined based on the second step size and the sampling power parameter of the current sampling point;

[0021] Obtain the battery parameters of the vehicle to be charged, and determine the anti-disturbance factor based on the golden ratio parameter and / or the battery parameters of the vehicle to be charged;

[0022] Based on the anti-disturbance factor, the second search power range, and the golden section parameters, multiple first-type golden sampling division points are determined;

[0023] Obtain the efficiency parameters of the plurality of first-class gold sampling division points corresponding to the plurality of first-class gold sampling division points.

[0024] In one possible embodiment, the iterative calculation operation based on the plurality of golden section efficiency points and the plurality of first section point efficiency parameters to determine the convergence efficiency parameters includes:

[0025] The third search power range and the efficiency parameters of the multiple first segmentation points are determined based on the efficiency parameters of the multiple first segmentation points.

[0026] Based on the third search power interval and the golden section parameters, determine the fourth search power interval and the efficiency parameters of multiple third division points;

[0027] The first convergence judgment result is determined based on the efficiency parameter of the second segmentation point, the efficiency parameter of the third segmentation point, and the preset efficiency difference threshold.

[0028] The second convergence judgment result is determined based on the fourth search power range and the preset range threshold.

[0029] The convergence efficiency parameter is determined based on the first convergence judgment result and the second convergence judgment result.

[0030] In one possible embodiment, determining the power parameter for the next cycle based on the convergence efficiency parameter and performing tracking control includes:

[0031] The power parameters for the next cycle are determined based on the convergence efficiency parameters.

[0032] The power of the target charging pile is controlled to be the power parameter of the next cycle;

[0033] Obtain the third charging efficiency parameter and the second power parameter;

[0034] The efficiency change rate parameter is determined based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter.

[0035] Dynamic control decisions are determined based on the efficiency change rate parameter.

[0036] Tracking and control are performed based on the dynamic control decision and the power parameters of the next cycle.

[0037] Secondly, embodiments of this application provide a charging efficiency optimization device for charging piles, applied to a controller of a charging efficiency optimization system, wherein the charging efficiency optimization system further includes multiple target charging piles, and the device includes:

[0038] The acquisition module is used to acquire multiple power parameters for the current cycle, and to determine multiple first charging efficiency parameters based on the multiple power parameters for the current cycle;

[0039] The first search module is used to determine multiple second charging efficiency parameters for the first long search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter.

[0040] The second search module is used to determine multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step length search stage based on the multiple second charging efficiency parameters if the second charging efficiency parameters meet the flow conditions of the second step length search stage.

[0041] The convergence iteration module is used to perform iterative calculation operations based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine the convergence efficiency parameters.

[0042] The tracking and control module is used to determine the power parameters for the next cycle based on the convergence efficiency parameters and to perform tracking and control so as to maximize the operating efficiency of the charging pile in the next cycle.

[0043] Thirdly, embodiments of this application provide a computer-readable storage medium storing a charging efficiency optimization program for a charging pile. The charging efficiency optimization program for the charging pile includes execution instructions. When a processor executes the execution instructions stored in the memory, the processor performs some or all of the steps described in the first aspect.

[0044] Fourthly, embodiments of this application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and when the processor executes the one or more programs, the processor executes some or all of the instructions of the steps described in the first aspect of the embodiments of this application.

[0045] Fifthly, embodiments of this application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of embodiments of this application. The computer program product may be a software installation package.

[0046] By implementing the embodiments of this application, the controller of the charging efficiency optimization system acquires multiple power parameters for the current cycle, and determines multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; determines multiple second charging efficiency parameters for the first step length search stage based on the multiple power parameters, the first charging efficiency parameters, multiple efficiency thresholds, and the first power parameters; if the second charging efficiency parameters meet the flow conditions of the second step length search stage, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step length search stage based on the multiple second charging efficiency parameters; performs iterative calculation operations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine converged efficiency parameters; and determines the power parameters for the next cycle based on the converged efficiency parameters and performs tracking and adjustment to maximize the charging pile's operating efficiency in the next cycle. Thus, under fixed demand, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the charging pile efficiency range limitation, enabling the equipment to continuously operate in the maximum efficiency range, and reducing overall losses. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0048] Figure 1 This is a schematic diagram of the architecture of a charging efficiency optimization system provided in an embodiment of this application;

[0049] Figure 2 This is a flowchart illustrating a method for optimizing the charging efficiency of a charging pile according to an embodiment of this application.

[0050] Figure 3 This is a schematic diagram illustrating the acquisition of power parameters for a charging efficiency optimization method for a charging pile provided in an embodiment of this application.

[0051] Figure 4 This is a flowchart illustrating the determination of convergence efficiency parameters in a charging efficiency optimization method for a charging pile, as provided in an embodiment of this application.

[0052] Figure 5 This is a flowchart illustrating another method for optimizing the charging efficiency of a charging pile proposed in an embodiment of this application.

[0053] Figure 6This is a schematic diagram of the structure of a charging efficiency optimization device for a charging pile provided in an embodiment of this application;

[0054] Figure 7 This is a schematic diagram of another charging efficiency optimization device for a charging pile provided in an embodiment of this application;

[0055] Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0056] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0057] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or electronic device that includes a series of steps or units is not limited to the listed steps or units, but in an alternative example also includes steps or units not listed, or in an alternative example also includes other steps or units inherent to these processes, methods, products, or electronic devices.

[0058] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0059] With the continuous increase in the number of new energy vehicles, the industry is currently focusing on "power race" and "density improvement." For example, the industry is using megawatt-level supercharging to improve charging speed and network density. However, this has not fundamentally solved the problem of a narrow efficiency range: the peak efficiency of charging piles is only achieved within a narrow voltage / power range, while the actual charging range for users is much wider, resulting in a significant decrease in average efficiency; thermal management energy consumption surges during high-power charging; to ensure high compatibility, operators need to configure modules with a wide voltage range, but the efficiency of traditional topologies drops sharply under non-standard operating conditions, causing energy waste on the grid side and higher electricity prices for users. In summary, under complex operating conditions with a wide power range, varying ambient temperature, and multiple voltage requirements, there are still problems such as insufficient overall efficiency of the charging system, unstable high-power output, and high overall losses.

[0060] To address the aforementioned issues, this application provides a method and related apparatus for optimizing the charging efficiency of charging piles. Under fixed demand, it can achieve real-time fitting of the optimal voltage-power solution, breaking through the efficiency range limitations of charging piles, enabling the equipment to continuously operate within the maximum efficiency range, and reducing overall losses.

[0061] The charging efficiency optimization method and related apparatus for charging piles provided in this application can be applied to, for example... Figure 1 Please refer to the charging efficiency optimization system shown. Figure 1 , Figure 1 This is a schematic diagram of the architecture of a charging efficiency optimization system provided in an embodiment of this application. The charging efficiency optimization system 100 includes a target charging pile 120 and a controller 110. The target charging pile 120 can communicate with the controller 110 through a network.

[0062] In this solution, the target charging pile 120 is used for charging and discharging and interacts with the controller 110. The target charging pile 120 may have a user interface, allowing users to easily input their specified charging needs. In some cases, the target charging pile 120 may also be a simulated charging pile, which is essentially equivalent to an actual charging pile in use and is used for simulation training. The controller 110 refers to a computer or microcontroller used to handle large amounts of computational tasks and store data. In this solution, the controller 110 is equipped with a charging efficiency optimization program for optimizing the charging efficiency of the charging pile. The controller 110 can also be used to collect data during the operation of the target charging pile 120 to execute the charging optimization method.

[0063] Based on this, this application provides a method and related apparatus for optimizing the charging efficiency of a charging pile. The following is a detailed description of this application with reference to the accompanying drawings.

[0064] Please see Figure 2 , Figure 2This is a flowchart illustrating a method for optimizing the charging efficiency of a charging pile according to an embodiment of this application. The method is applied to the controller of a charging efficiency optimization system, which further includes multiple target charging piles, such as... Figure 2 As shown, the method includes the following steps:

[0065] S210, acquire multiple power parameters for the current cycle, and determine multiple first charging efficiency parameters based on the multiple power parameters for the current cycle.

[0066] The current period can be a pre-set time interval. Multiple power parameters include the output power acquired based on the target charging pile's output voltage. This target charging pile's output voltage is dynamically changing. Within the preset output voltage range, the voltage is adjusted in a stepped manner within the current period. In some cases, each voltage adjustment is maintained for a preset time duration, and multiple output powers at that corresponding voltage are acquired within 2 minutes. This preset time duration can be 2 minutes or other durations, selected according to the actual application, and is not limited here. The multiple power parameters can include at least one of input power parameters and output power parameters.

[0067] The first charging efficiency parameter can be determined by multiple power parameters, specifically by calculating the ratio between the output power parameter and the input power parameter.

[0068] Optionally, the aforementioned power parameters can be obtained directly, or calculated based on at least two of the obtained input voltage, output voltage, input current, and output current. The output power parameter can be determined by multiplying the output voltage and output current, and the input power parameter can be determined by multiplying the input voltage and input current.

[0069] In one possible embodiment, the multiple power parameters of the current period include multiple current input power parameters and multiple current output power parameters. Determining multiple first charging efficiency parameters based on the multiple power parameters of the current period includes: determining multiple current charging efficiencies based on the multiple current input power parameters and the multiple current output power parameters; obtaining the power grid frequency period, where the power grid frequency period is the operating frequency of the power grid where the target charging pile is located; determining an attenuation weighting factor based on the power grid frequency period; and determining the multiple first charging efficiency parameters based on the attenuation weighting factor and the multiple current charging efficiencies, wherein the multiple first charging efficiency parameters are associated with and correspond to the multiple current input power parameters and / or the multiple current output power parameters.

[0070] The multiple current input power parameters include the input power parameters of the target charging pile and the output power parameters of the target charging pile to the outside world (the vehicle to be charged). Optionally, multiple current charging efficiencies can be determined by dividing the output power parameters by the input power parameters, constructing a characteristic curve of charging efficiency versus output power, replacing traditional voltage / current control, and realizing a shift from "power-oriented" to "efficiency-oriented".

[0071] After determining the current charging efficiency, dynamic filtering is performed by adding an attenuation weighting factor to suppress power oscillations caused by metering fluctuations, thereby determining the first charging efficiency parameter. Specifically, the attenuation weighting factor is correlated with the power grid frequency period. The power grid frequency refers to the nominal AC frequency during normal operation of the power system, and the power grid frequency period is the time required for a complete AC waveform corresponding to the power frequency.

[0072] Optionally, the attenuation weighting factor based on the power grid frequency cycle can be determined using the following method: First, determine the half-cycle based on the power grid frequency cycle. This half-cycle value matches the characteristics of harmonic interference, precisely corresponding to the complete cycle of the second harmonic (twice the length of the power grid frequency cycle), and is closely related to the symmetry characteristics of common odd harmonics within the half-cycle. Then, determine the attenuation coefficient based on Monte Carlo simulation experiments. Finally, determine the attenuation weighting factor by performing an exponential operation based on the attenuation coefficient and the half-cycle. Optionally, the above method of determining the attenuation weighting factor by performing an exponential operation based on the attenuation coefficient and the half-cycle can be: ,in, The attenuation coefficient is... It is a half-cycle. This is the attenuation weighting factor. Optionally, the attenuation coefficient can be determined via Monte Carlo simulation at a given time step Δt. It is the optimal or effective value for this specific application scenario.

[0073] For example, the harmonic interference characteristics of the power grid with a power frequency period of 50Hz / 20ms are used to determine the half-cycle. The attenuation coefficient was determined based on Monte Carlo simulation with a duration of 10ms. In some possible cases, the attenuation coefficient can also be pre-calculated using pre-set grid-related parameters and can be directly used; this is not a limitation here.

[0074] Optionally, determining the plurality of first charging efficiency parameters based on the attenuation weighting factor and the plurality of current charging efficiencies can be achieved through the following process: weighted averaging of the current charging efficiencies of the time-series data. This can be achieved using the following formula:

[0075] ;

[0076] in, The first charging efficiency parameter mentioned above.

[0077] For an example, please refer to Figure 3 , Figure 3 This is a schematic diagram illustrating the acquisition of power parameters for a charging pile charging efficiency optimization method provided in an embodiment of this application. Figure 3 As shown, in the current cycle, the target charging pile is controlled to perform stepped voltage regulation within the output voltage range (300V–1000V), for example, 400V, 700V, 1000V. Each voltage regulation point is maintained at 400V, 700V, 1000V for 2 minutes to ensure stable operation. The power parameters corresponding to each voltage regulation point within 2 minutes are obtained as multiple power parameters in the current cycle.

[0078] As can be seen, in this embodiment, the reliability of the data is improved and efficient dynamic response is achieved by adding an attenuation weight factor. The attenuation weight factor determined based on the power grid frequency cycle can accurately match the power grid, improve scenario adaptability and measurement accuracy.

[0079] S220, based on the plurality of power parameters, the first charging efficiency parameter, the plurality of efficiency thresholds, and the first power parameter, determine a plurality of second charging efficiency parameters for the first step long search phase.

[0080] The first step of the long search phase is a large step search phase, which is used to initially and quickly locate the peak range. In the first step of the long search phase, a traversal search is performed, and a preliminary large range is determined based on multiple power parameters, multiple efficiency thresholds, a first power parameter, and a first charging efficiency parameter. The second charging efficiency parameter is associated with the boundary of the preliminary large range.

[0081] The multiple efficiency thresholds can be pre-set and are used to compare with a first charging efficiency parameter or with the mathematical calculation results of multiple first charging efficiency parameters, constraining whether the first long search phase ends and proceeds to the next search phase. The aforementioned first power parameter can be a rated power parameter, associated with the search step size in the first long search phase.

[0082] In one possible embodiment, determining multiple second charging efficiency parameters for the first step length search phase based on the plurality of power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter includes: determining a first step length based on the first power parameter, and determining a first search power range based on the plurality of power parameters; performing a first search operation based on the first step length, the first search power range, and initialization information, wherein the initialization information includes a starting power parameter; the first search operation includes: searching within the first search power range based on the starting power and the first step length to determine a sampling power parameter and a sampling efficiency parameter for the current sampling point, wherein the sampling power parameter for the current sampling point is one of the plurality of power parameters, and the sampling efficiency parameter for the current sampling point is at least one of the plurality of second charging efficiency parameters.

[0083] Here, the first step length is the step size used for traversal search in the first step length search phase, which can be determined by the first power parameter. For example, the first step length can be calculated based on the first power parameter, such as a first step length of p × P. rated The above p is a preset coefficient, P rated The first power parameter is the rated power; for example, p can be 5%, meaning the first search length is 5% of the rated power. During the first search length phase, an initial search range, i.e., the first search power range, also needs to be defined. This first search power range can be determined based on multiple power parameters. For example, it can be determined based on the maximum and minimum values ​​among multiple power parameters. In some cases, it can also be determined by dynamically adjusting the range based on a preset search range and multiple power parameters.

[0084] The initialization information includes the starting power parameter, which is the starting point of the search. The search begins with the starting power parameter and increases by the first step length. For example, the starting power parameter can be 0.

[0085] In this process, after determining the initial power parameter, the first search power range, and the first step length, a first search operation is performed within the first search power range, starting with the initial power parameter and gradually increasing the first step length. Each time the step length is increased, at least one of the current sampling efficiency parameter and sampling power parameter is measured or acquired. The sampling efficiency parameter and sampling power parameter correspond to one of the aforementioned plurality of second charging efficiency parameters, and the sampling power parameter corresponds to one of the aforementioned plurality of first power parameters. The data pairs of the sampling power parameter and sampling efficiency parameter are stored to provide a basis for subsequent peak value determination.

[0086] For example, determine the length of the first step as ΔP = 5%P ratedThe first search power interval is determined to be [P]. low P high The initial power parameter is 0, and it increases by ΔP from 0. The sampling power parameter for the next sampling point is P. prev =P curr +ΔP, where P curr P represents the sampling power parameter of the current sampling point. In the first search step, P... curr =Initial power parameter=0. At the next sampling point, the sampling power parameter is acquired or the corresponding sampling efficiency parameter η is collected. curr Storage (P) curr , η curr Data pairs.

[0087] As can be seen, in this embodiment, η is used as a direct feedback variable to quickly locate within the first step search interval with a large step size, which narrows the range for subsequent fine tracking, effectively improving the peak positioning speed, thereby improving the peak positioning speed and quickly and accurately determining the maximum charging efficiency.

[0088] S230, if the second charging efficiency parameter meets the flow conditions of the second step size search stage, determine multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step size search stage based on the multiple second charging efficiency parameters.

[0089] The second charging efficiency parameter is the sampling efficiency parameter obtained in the first step long search phase. Based on the second charging efficiency parameter, it is determined whether to enter the second step long search phase. The transition conditions of the second step long search phase are preset adjustments used to constrain whether to enter the second step long search phase, which may specifically include constraints on the second charging efficiency parameter.

[0090] If the second charging efficiency parameter meets the transition conditions of the second step search stage, then the second step search stage is entered, and the second step search traversal is performed.

[0091] Optionally, if the second charging efficiency parameter does not meet the transition conditions of the second step length search stage, the first step length search operation is performed repeatedly, and the sampling power parameter is increased by the first step length to obtain the sampling efficiency parameter of the next sampling point.

[0092] In the second step search phase, a sampling method based on the golden section parameters is used to determine multiple golden section efficiency points and the efficiency parameter of the first division point. The aforementioned golden section efficiency points are interval-related parameters, which can be understood as interval boundary parameters, and the efficiency parameter of the first division point is the efficiency peak point.

[0093] In one possible embodiment, the second charging efficiency parameter includes at least one of the sampling efficiency parameter of the next sampling point and the sampling efficiency parameter of the current sampling point. The transition conditions of the second step search phase include: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or the difference between the sampling efficiency parameter of the current sampling point and the sampling efficiency parameter of the next sampling point is greater than a preset efficiency decrease threshold.

[0094] Wherein, if the sampling efficiency parameter of the current sampling point is detected to satisfy η curr >η thr Time (where η) thr (Based on a preset efficiency threshold), it can be determined that the system has reached the target efficiency range, triggering a transition to the second step search stage; when the efficiency decay between adjacent sampling points satisfies |η curr –η prev |>Δη thr (Δη) thr (for a preset efficiency degradation threshold), and η curr <η prev If the efficiency is determined to be in a rapidly declining range, the current first step of the long search phase is terminated early, and the process moves to the second step of the long search phase. Here, Δη... thr Preset, example, η thr It can be 90%, Δη thr It can be 2%. Satisfying either of the above two conditions constitutes satisfying the transition condition for the second compensation search phase. If neither of the above two conditions is satisfied, proceed to the next cycle, letting η... prev =η curr Continue the cycle of the first long search phase until the conditions for the second long search phase are met.

[0095] For example, the default η thr =90%, Δη thr =2%, in obtaining (P) curr , η curr Data pairs and η prev Then, based on η curr η prev and Δη thr Compare. Condition 1: If η curr >η thr =90%, indicating that we have entered the high-efficiency zone and need to switch to micro-step fine search, i.e., the second step search stage; Condition 2: If the efficiency difference between two adjacent steps is |η curr -η prev |>Δη thr =2%, and η curr <η prevThis indicates that efficiency has begun to decline significantly, possibly approaching the peak region, triggering a micro-step size switch. It requires switching to a micro-step size fine search, i.e., the second step size search stage. If either condition 1 or condition 2 is not met, proceed to the next cycle, letting η... prev =η curr Continue the cycle of the first long search phase until the conditions for the second long search phase are met.

[0096] As can be seen, in this embodiment, by setting two conditional boundaries, the range for entering the second step search stage can be made more accurate, effectively improving the peak positioning speed, thereby improving the peak positioning speed and quickly and accurately determining the maximum charging efficiency.

[0097] In one possible embodiment, determining the multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step size search stage based on the multiple second charging efficiency parameters includes: determining a second step size based on the first power parameter, and determining a second search power range based on the second step size and the sampling power parameter of the current sampling point; obtaining the battery parameters of the vehicle to be charged, and determining an anti-disturbance factor based on the golden section parameter and / or the battery parameters of the vehicle to be charged; determining multiple first-type golden sampling segmentation points based on the anti-disturbance factor, the second search power range, and the golden section parameter; and obtaining the multiple first segmentation point efficiency parameters corresponding to the multiple first-type golden sampling segmentation points.

[0098] The second step size is the step size used for traversal searching in the second step search phase, and it can be determined by the first power parameter. For example, the second step size can be calculated based on the first power parameter, such as q×P. rated The above q is a preset coefficient, P rated The first power parameter is the rated power; for example, q can be 0.5%, meaning the first step size is 0.5% of the rated power. In the second step search phase, a search interval needs to be defined, i.e., the second search power interval. This second search power interval can be determined based on the sampling power parameter of the current sampling point. For example, it can be determined by expanding a certain range in both positive and negative directions from the sampling power parameter of the current sampling point as the center. In some cases, it can be determined by expanding the range based on the golden ratio parameter to determine the second power search interval.

[0099] Optionally, the left boundary P of the second power search interval low and right boundary P high The left boundary P can be determined through the following process. low Equals the sampling power parameter of the current sampling point minus N times q×P rated Right boundary P highEquals the sampling power parameter of the current sampling point plus N times q×P rated The second power search interval is [P] curr -N×q×P rated P curr +N×q×P rated Preferably, N can be 2, q is 0.5%, that is, P low =P curr -2×0.5%×P rated P high =P curr +2×0.5%×P rated The second power search interval is [P] curr -2×0.5%×P rated P curr +2×0.5%×P rated ].

[0100] The disturbance resistance factor is used to prevent local extreme value trapping and suppress temperature rise fluctuations. The disturbance factor can be determined based on the golden ratio parameter and the battery parameters of the vehicle being charged at the target charging station. The battery parameters include the thermal time constant. Optionally, the battery parameters of the vehicle being charged at the target charging station can be preset or obtained at the start of charging. For example, if preset, it can be the thermal time constant of at least one typical power battery, such as a preset thermal time constant τ≈100s; if obtained at the start of charging, the vehicle information of the vehicle being charged is acquired, and the thermal time constant of that vehicle is directly obtained, or the corresponding thermal time constant is matched to the database based on the vehicle model.

[0101] Optionally, the anti-disturbance factor can be determined by the following steps: determining the disturbance period T based on the thermal time constant, determining the disturbance amplitude A, which can be preset or determined based on the vehicle information of the vehicle to be charged; and determining the anti-disturbance factor based on the disturbance period T, the disturbance amplitude A, and the golden section parameter.

[0102] Specifically, the anti-disturbance factor β = 0.618 + 0.05•sin(2πt / T), where 0.618 is the golden ratio parameter and 0.05 is the disturbance amplitude A.

[0103] For example, if the thermal time constant is τ≈100s, the disturbance period is T=0.1τ≈10s, and the disturbance amplitude is A=0.05.

[0104] Among them, multiple first-type golden sampling division points include the first golden sampling division point and the second golden sampling division point, and the second search power interval includes the left boundary P. low and right boundary P high Based on the anti-disturbance factor and the left boundary P of the second search power intervallow and right boundary P high Determining multiple first-type golden section points based on the golden section parameters may include the following steps: based on the right boundary P high Subtract the anti-disturbance factor and the left boundary P low and right boundary P high The product of the differences determines the first golden sampling point, based on the left boundary P. low Adding the anti-disturbance factor and the left boundary P low and right boundary P high The product of the differences determines the second golden sampling point. Example: First golden sampling point a = P high -β×(P high -P low The second golden sampling point b=P low +β×(P high -P low ).

[0105] Specifically, based on the multiple first-type gold sampling division points (first gold sampling division point and second gold sampling division point) obtained above, multiple corresponding first division point efficiency parameters are detected or obtained. The first division point efficiency parameters include the efficiency value η corresponding to the first gold sampling division point. a The efficiency parameter for the second segmentation point includes the efficiency value η corresponding to the second golden sampling segmentation point. b .

[0106] For example, if the second search power range is [296.4, 303.6], then a = 303.6 - 0.618β × 7.2, b = 296.4 + 0.618β × 7.2.

[0107] As can be seen, in this embodiment, the mathematical optimization algorithm (golden section method) is deeply integrated with engineering physical constraints (battery thermodynamics). Through periodic anti-disturbance weighting factors and stable measurement strategies, safe and efficient parameter search is achieved, which effectively improves the peak positioning speed and accuracy, thereby improving the peak positioning speed and accuracy to quickly and accurately determine the maximum charging efficiency.

[0108] In one possible embodiment, determining the multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step size search stage based on the multiple second charging efficiency parameters includes: determining the second step size based on the first power parameter; determining the second search power range based on the second step size and the sampling power parameter of the current sampling point; determining multiple first-type golden sampling segmentation points based on the second search power range and the golden section parameter; and obtaining the multiple first segmentation point efficiency parameters corresponding to the multiple first-type golden sampling segmentation points.

[0109] The second step size is the step size used for traversal searching in the second step search phase, and it can be determined by the first power parameter. For example, the second step size can be calculated based on the first power parameter, such as q×P. rated The above q is a preset coefficient, P rated The first power parameter is the rated power; for example, q can be 0.5%, meaning the first step size is 0.5% of the rated power. In the second step search phase, a search interval needs to be defined, i.e., the second search power interval. This second search power interval can be determined based on the sampling power parameter of the current sampling point. For example, it can be determined by expanding a certain range in both positive and negative directions from the sampling power parameter of the current sampling point as the center. In some cases, it can be determined by expanding the range based on the golden ratio parameter to determine the second power search interval.

[0110] Optionally, the left boundary P of the second power search interval low and right boundary P high The left boundary P can be determined through the following process. low Equals the sampling power parameter of the current sampling point minus N times q×P rated Right boundary P high Equals the sampling power parameter of the current sampling point plus N times q×P rated The second power search interval is [P] curr -N×q×P rated P curr +N×q×P rated Preferably, N can be 2, q is 0.5%, that is, P low =P curr -2×0.5%×P rated P high =P curr +2×0.5%×P rated The second power search interval is [P] curr -2×0.5%×P rated P curr +2×0.5%×P rated ].

[0111] Among them, multiple first-type golden sampling division points include the first golden sampling division point and the second golden sampling division point, and the second search power interval includes the left boundary P. low and right boundary P high Based on the left boundary P of the second search power interval low and right boundary P high Determining multiple first-type golden section points based on the golden section parameters may include the following steps: based on the right boundary P high Subtract the left boundary Plow and right boundary P high The difference determines the first golden sampling point, based on the left boundary P. low Add to the left boundary P low and right boundary P high The difference determines the second golden sampling point. Example: First golden sampling point a=P high -(P high -P low The second golden sampling point b=P low +(P high -P low ).

[0112] Specifically, based on the multiple first-type gold sampling division points (first gold sampling division point and second gold sampling division point) obtained above, multiple corresponding first division point efficiency parameters are detected or obtained. The first division point efficiency parameters include the efficiency value η corresponding to the first gold sampling division point. a The efficiency parameter for the second segmentation point includes the efficiency value η corresponding to the second golden sampling segmentation point. b .

[0113] For example, the second search power range is [296.4, 303.6], a = 303.6 - 0.618 × 7.2 ≈ 299.1 kW, b = 296.4 + 0.618 × 7.2 ≈ 300.9 kW.

[0114] As can be seen, in this embodiment, based on the mathematical optimization algorithm (golden section method), a safe and efficient parameter search is achieved through periodic anti-disturbance weighting factors and stable measurement strategies, which effectively improves the peak positioning speed and accuracy, thereby improving the peak positioning speed and accuracy to quickly and accurately determine the maximum charging efficiency.

[0115] S240, perform iterative calculations based on the multiple golden section efficiency points and the efficiency parameters of the multiple first section points to determine the convergence efficiency parameters.

[0116] After determining the efficiency parameters of multiple dividing points, it is necessary to further determine whether the interval of the efficiency parameters of the golden section point is small enough to ensure that the peak value to be sought is optimal. In this process, it is necessary to judge the interval convergence and perform iterative calculations to ensure the interval convergence and finally determine the converged peak value, i.e., the convergence efficiency parameter.

[0117] In one possible embodiment, please refer to Figure 4 , Figure 4 This is a flowchart illustrating the determination of convergence efficiency parameters in a charging efficiency optimization method for a charging pile, as provided in an embodiment of this application. Figure 4As shown, the iterative calculation operation based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine the convergence efficiency parameters includes:

[0118] S410, determine the third search power range and the efficiency parameters of the second segmentation points based on the efficiency parameters of the plurality of first segmentation points.

[0119] S420, determine the fourth search power range and multiple third segmentation point efficiency parameters based on the third search power range and the golden ratio parameter.

[0120] Specifically, the process involves iterating based on the golden ratio parameters within the third search interval. Optionally, this iterative process can also determine the fourth search power interval and multiple efficiency parameters for the third segmentation points by further iterating based on the golden ratio parameters within the third search interval. This may include:

[0121] S421, Obtain the battery parameters of the vehicle to be charged, and determine the anti-disturbance factor based on the golden ratio parameters and / or the battery parameters of the vehicle to be charged.

[0122] S422, Based on the third search power range, the golden section parameter, and / or the anti-disturbance factor, determine a plurality of second-type golden sampling division points;

[0123] S423, obtain the efficiency parameters of the multiple third segmentation points and the fourth search power range corresponding to the multiple second-type gold sampling segmentation points;

[0124] Specifically, the third golden sampling point is determined based on the difference between the right boundary and the left and right boundaries, and the fourth golden sampling point is determined based on the left boundary plus the difference between the left and right boundaries.

[0125] Specifically, based on the multiple second-type golden sampling division points (third golden sampling division points and fourth golden sampling division points) obtained above, multiple corresponding third division point efficiency parameters are detected or obtained. The first division point efficiency parameter includes the efficiency value η corresponding to the third golden sampling division point. c The efficiency parameter for the fourth segmentation point includes the efficiency value η corresponding to the fourth golden sampling segmentation point. d .

[0126] The above steps are similar to the embodiments included in step S230. Please refer to the above embodiments for details, which will not be repeated here.

[0127] S430, determine the first convergence judgment result based on the efficiency parameter of the second segmentation point, the efficiency parameter of the third segmentation point, and the preset efficiency difference threshold.

[0128] S440, determine the second convergence judgment result based on the fourth search power range and the preset range threshold; determine the convergence efficiency parameter based on the first convergence judgment result and the second convergence judgment result.

[0129] The third search power interval is based on the left and right boundaries of the second search power interval, and the efficiency parameter of the first segmentation point includes the efficiency value η corresponding to the first golden sampling segmentation point. a The efficiency parameter for the second segmentation point includes the efficiency value η corresponding to the second golden sampling segmentation point. b Further determined boundary values.

[0130] Optionally, determining the third search power interval and the multiple second segmentation point efficiency parameters based on the multiple first segmentation point efficiency parameters includes: if η a >η b This indicates that the peak value is in the left half of the interval, thus determining the third search power interval as [P]. low ,b];If η b ≥η a This indicates that the peak value is in the right half of the interval, thus determining the third search power interval as [a, P]. high ].

[0131] The determination of the first convergence judgment result based on the efficiency parameter of the second segmentation point, the efficiency parameter of the third segmentation point, and a preset efficiency difference threshold includes: determining the relationship between the efficiency difference (efficiency parameter of the second segmentation point and efficiency parameter of the third segmentation point) between two adjacent measurements and the preset efficiency difference threshold. Specifically, |η new -η old | < ε indicates that the efficiency is close to its peak, where ε is a preset efficiency difference threshold, preferably ε = 0.1%; if |η new -η old If |<ε, then the first convergence judgment result is determined as the first result; otherwise, if |η is not satisfied... new -η old If |<ε, then the result of the first convergence judgment is determined to be the second result.

[0132] The determination of the second convergence judgment result based on the fourth search power interval and the preset interval threshold includes: P in the fourth search power interval high -P low <ξ, where ξ is a preset interval threshold, ξ=2×q×P rated =2×0.5%×P rated This indicates that the power adjustment accuracy has reached the microstep level; if P is satisfied... high -P low If <ξ, then the second convergence judgment result is determined as the third result; if P is not satisfied... high -P lowIf ξ < , then the second convergence judgment result is determined to be the fourth result.

[0133] If either the first convergence judgment result is the first result, or the second convergence judgment result is the third result, then it is determined that the convergence condition has been met, the iterative calculation operation is terminated, and the current interval is locked to determine the convergence efficiency parameter.

[0134] Determining the convergence efficiency parameter includes: locking the midpoint of the current interval as the point of maximum efficiency, and the convergence efficiency parameter η. max And determine the corresponding power P at this time. opt .

[0135] It should be noted that the above iteration can be repeated. The example only illustrates the process of iterating twice. If the first convergence judgment result and the second convergence judgment result do not meet the convergence condition, the iteration continues based on this until the first convergence judgment result is the first result or the second convergence judgment result is the third result within the preset iteration number threshold, satisfying either one.

[0136] As can be seen, in this embodiment, by combining two-level interval contraction with two-dimensional convergence judgment, the optimal efficiency point usable in engineering is efficiently output while ensuring the safe thermal management of the power battery. The micro-step golden section multi-level iterative fine tracking improves the peak positioning speed.

[0137] In one possible embodiment, if the iterative calculation operation fails to converge after more than a preset iteration threshold (preferably 10 times), the fault diagnosis module is triggered, and the process reverts to the normal charging parameter point (such as the smaller value between rated power and required voltage / current).

[0138] S250, based on the convergence efficiency parameter, determine the power parameter for the next cycle and perform tracking and adjustment to maximize the operating efficiency of the charging pile in the next cycle.

[0139] Once the convergence efficiency parameter is determined, it can be directly used as the subsequent power parameter for charging the vehicle to be charged, and then iteratively optimized based on it in the next cycle.

[0140] In one possible embodiment, determining the power parameter for the next cycle based on the convergence efficiency parameter and performing tracking control includes: determining the power parameter for the next cycle based on the convergence efficiency parameter; controlling the power of the target charging pile to be the power parameter for the next cycle; obtaining a third charging efficiency parameter and a second power parameter; determining an efficiency change rate parameter based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter; determining a dynamic control decision based on the efficiency change rate parameter; and performing tracking control based on the dynamic control decision and the power parameter for the next cycle.

[0141] After determining the maximum efficiency point (converged efficiency parameter), dynamic maintenance and fine-tuning are achieved by real-time monitoring of the efficiency change rate (dη / dP), entering the "tracking and maintenance" stage. First, the parameters for the next cycle are obtained by outputting the converged efficiency parameter, namely the third charging efficiency parameter and the second power parameter. Then, based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter, derivative calculations are performed to determine the efficiency change rate parameter. Based on this efficiency change rate parameter, a dynamic control decision is determined, which includes at least one of maintaining the current power, increasing the output power, and decreasing the output power. Then, based on the dynamic control decision, tracking and control are performed on the power parameters for the next cycle.

[0142] Optionally, based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter, a derivative calculation is performed to determine the efficiency change rate parameter, including: efficiency change rate parameter dη / dP=(η k –η k-1 ) / (P k –P k-1 ), where η k P is the third charging efficiency parameter. k η is the second power parameter. k-1 P is the first charging efficiency parameter. k-1 This is the first power parameter.

[0143] Optionally, determining the dynamic control decision based on the efficiency change rate parameter includes: if |dη / dP| < a first change rate threshold, the dynamic control decision is to maintain the current power, wherein the first change rate threshold is preferably 0.1%; if dη / dP > a second change rate threshold, the dynamic control decision is to increase the output power, wherein the second change rate threshold is preferably 0.3%; if dη / dP < a negative second change rate threshold, the dynamic control decision is to reduce the output power.

[0144] Optionally, the specific values ​​for increasing or decreasing the output power mentioned above can be preset or determined by an algorithm, and are not limited here.

[0145] As can be seen, in this embodiment, by charging at a determined maximum efficiency value and then dynamically fine-tuning, the optimal voltage-power solution can be fitted in real time, breaking through the efficiency range limitation of the charging pile, enabling the equipment to continuously operate in the maximum efficiency range, and reducing overall losses.

[0146] For the example, please refer to it again. Figure 3The terminal charging gun is inserted into the vehicle simulator (target charging vehicle), and charging is started. The BMS requires voltage and current at the maximum values ​​within the charging pile's capacity range (1000V, 400A). The target charging pile is controlled to output voltage in a stepped manner within the range of 300V–1000V, for example, 400V, 700V, and 1000V. Each voltage adjustment point is maintained at 400V, 700V, and 1000V for 2 minutes. The power parameters corresponding to each voltage adjustment point within 2 minutes are obtained and used as multiple power parameters in the current cycle to generate η-P. out The system initiates a variable step size search algorithm, initially using a large step size of 18kW (5%P_rated) to quickly locate the 90% efficiency zone (the first search power zone). After entering the 90% efficiency zone, it switches to a micro step size of 1.8kW to determine the second search power zone. Iterates three times using the golden section method to lock the peak efficiency point (1000V / 78.8kW, efficiency 95.8%). The entire process takes 1.2 seconds. During charging, the efficiency change rate (dη / dP) is monitored in real time. The maximum efficiency tracking algorithm is used to dynamically maintain the maximum efficiency output of the charging pile, entering the continuous "tracking and maintaining" maximum output efficiency stage.

[0147] As can be seen, in this embodiment, the controller of the charging efficiency optimization system acquires multiple power parameters for the current cycle, and determines multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; determines multiple second charging efficiency parameters for the first step length search stage based on the multiple power parameters, the first charging efficiency parameters, multiple efficiency thresholds, and the first power parameters; if the second charging efficiency parameters meet the flow conditions of the second step length search stage, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step length search stage based on the multiple second charging efficiency parameters; performs iterative calculation operations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine convergence efficiency parameters; and determines the power parameters for the next cycle based on the convergence efficiency parameters and performs tracking and adjustment to maximize the charging pile's operating efficiency in the next cycle. Thus, under fixed requirements, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the charging pile efficiency range limitation, enabling the equipment to continuously operate within the maximum efficiency range, and reducing overall losses.

[0148] Please refer to Figure 5 , Figure 5 This is a flowchart illustrating another method for optimizing the charging efficiency of a charging pile provided in this application embodiment, as shown below. Figure 5As shown, the method includes: acquiring multiple power parameters for the current cycle, determining multiple first charging efficiency parameters, performing a first-step length search, determining multiple second charging efficiency parameters for the first-step length search stage, determining whether the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or whether the difference between the sampling efficiency parameter of the current sampling point and the sampling efficiency parameter of the next sampling point is greater than a preset efficiency decrease threshold. If yes, proceed to the second-step length search stage; if no, repeat the first-step length search stage. In the second-step length search stage, determine multiple golden section efficiency points and multiple first section point efficiency parameters for the second-step length search stage, perform iterative calculation operations based on the multiple golden section efficiency points and multiple first section point efficiency parameters, determine the first convergence judgment result, determine whether convergence has occurred, if yes, determine the power parameters for the next cycle based on the converged efficiency parameters and perform tracking and control; if no, repeat the second-step length search stage for iteration until convergence or the number of iterations exceeds a preset iteration number threshold, then stop the iteration and end the process.

[0149] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a charging efficiency optimization device for a charging pile according to an embodiment of this application. The charging efficiency optimization device 600 for the charging pile includes: an acquisition module 610, a first search module 620, a second search module 630, a convergence iteration module 640, and a tracking and control module 650.

[0150] The system includes: an acquisition module 610 for acquiring multiple power parameters for the current cycle and determining multiple first charging efficiency parameters based on these parameters; a first search module 620 for determining multiple second charging efficiency parameters for a first step-long search phase based on the multiple power parameters, the first charging efficiency parameters, multiple efficiency thresholds, and the first power parameters; a second search module 630 for determining multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step-long search phase if the second charging efficiency parameters meet the flow conditions of the second step-long search phase; a convergence iteration module 640 for performing iterative calculations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine convergence efficiency parameters; and a tracking and control module 650 for determining the power parameters for the next cycle based on the convergence efficiency parameters and performing tracking and control to maximize the charging pile's operating efficiency in the next cycle.

[0151] In one possible embodiment, the plurality of power parameters of the current cycle includes a plurality of current input power parameters and a plurality of current output power parameters. The acquisition module 610, in determining the plurality of first charging efficiency parameters based on the plurality of power parameters of the current cycle, is specifically used for:

[0152] Multiple current charging efficiencies are determined based on the multiple current input power parameters and the multiple current output power parameters;

[0153] The power grid frequency period is obtained, which is the operating frequency of the power grid where the target charging pile is located.

[0154] The attenuation weighting factor is determined based on the power frequency cycle of the power grid.

[0155] The plurality of first charging efficiency parameters are determined based on the attenuation weighting factor and the plurality of current charging efficiencies, and the plurality of first charging efficiency parameters are associated with the plurality of current input power parameters and / or the plurality of current output power parameters.

[0156] In one possible embodiment, the first search module 620, in determining the plurality of second charging efficiency parameters for the first long search phase based on the plurality of power parameters, the first charging efficiency parameter, the plurality of efficiency thresholds, and the first power parameters, is specifically configured to:

[0157] The first step length is determined based on the first power parameter, and the first search power range is determined based on the plurality of power parameters;

[0158] A first search operation is performed based on the first step length, the first search power range, and initialization information, wherein the initialization information includes the initial power parameter.

[0159] The first search operation includes: searching within the first search power range based on the starting power and the first step length to determine the sampling power parameter and the sampling efficiency parameter of the current sampling point, wherein the sampling power parameter of the current sampling point is one of the plurality of power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the plurality of second charging efficiency parameters.

[0160] In one possible embodiment, the second charging efficiency parameter includes at least one of the sampling efficiency parameter of the next sampling point and the sampling efficiency parameter of the current sampling point. The transition conditions of the second step search phase include: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or the difference between the sampling efficiency parameter of the current sampling point and the sampling efficiency parameter of the next sampling point is greater than a preset efficiency decrease threshold.

[0161] In one possible embodiment, the second search module 630, in determining the plurality of golden section efficiency points and the plurality of first section point efficiency parameters for the second step size search stage based on the plurality of second charging efficiency parameters, is specifically used for:

[0162] The second step size is determined based on the first power parameter, and the second search power interval is determined based on the second step size and the sampling power parameter of the current sampling point;

[0163] Obtain the battery parameters of the vehicle to be charged, and determine the anti-disturbance factor based on the golden ratio parameter and / or the battery parameters of the vehicle to be charged;

[0164] Based on the anti-disturbance factor, the second search power range, and the golden section parameters, multiple first-type golden sampling division points are determined;

[0165] Obtain the efficiency parameters of the plurality of first-class gold sampling division points corresponding to the plurality of first-class gold sampling division points.

[0166] In one possible embodiment, the convergence iteration module 640, in determining the convergence efficiency parameters through the iterative calculation operation based on the plurality of golden section efficiency points and the plurality of first section point efficiency parameters, is specifically used for:

[0167] The third search power range and the efficiency parameters of the multiple first segmentation points are determined based on the efficiency parameters of the multiple first segmentation points.

[0168] Based on the third search power interval and the golden section parameters, determine the fourth search power interval and the efficiency parameters of multiple third division points;

[0169] The first convergence judgment result is determined based on the efficiency parameter of the second segmentation point, the efficiency parameter of the third segmentation point, and the preset efficiency difference threshold.

[0170] The second convergence judgment result is determined based on the fourth search power range and the preset range threshold.

[0171] The convergence efficiency parameter is determined based on the first convergence judgment result and the second convergence judgment result.

[0172] In one possible embodiment, the tracking control module 650 is specifically configured to: determine the power parameter for the next cycle based on the convergence efficiency parameter and perform tracking control.

[0173] The power parameters for the next cycle are determined based on the convergence efficiency parameters.

[0174] The power of the target charging pile is controlled to be the power parameter of the next cycle;

[0175] Obtain the third charging efficiency parameter and the second power parameter;

[0176] The efficiency change rate parameter is determined based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter.

[0177] Dynamic control decisions are determined based on the efficiency change rate parameter.

[0178] Tracking and control are performed based on the dynamic control decision and the power parameters of the next cycle.

[0179] It is worth noting that the specific functional implementation of the charging efficiency optimization device 600 for the charging pile is described above. Figure 2 The description of the charging efficiency optimization method for the charging pile shown includes, for example, the acquisition module 610 for implementing the relevant content of S210, the first search module 620 for implementing the relevant content of S220, the second search module 630 for implementing the relevant content of S230, the convergence iteration module 640 for implementing the relevant content of S240, and the tracking and control module 650 for implementing the relevant content of S250. Each unit or module in the charging efficiency optimization device 600 of the charging pile can be individually or entirely merged into one or more other units or modules, or some of the units or modules can be further divided into multiple functionally smaller units or modules. This achieves the same operation without affecting the technical effect of the embodiments of the present invention. The above-mentioned units or modules are based on logical function division. In practical applications, the function of one unit (or module) is implemented by multiple units (or modules), or the function of multiple units (or modules) is implemented by one unit (or module).

[0180] As can be seen, the charging efficiency optimization device 600 for the charging pile described in this embodiment of the application obtains multiple power parameters for the current cycle through the controller of the charging efficiency optimization system, and determines multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; determines multiple second charging efficiency parameters for the first step length search stage based on the multiple power parameters, the first charging efficiency parameters, multiple efficiency thresholds, and the first power parameters; if the second charging efficiency parameters meet the flow conditions of the second step length search stage, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step length search stage based on the multiple second charging efficiency parameters; performs iterative calculation operations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine convergence efficiency parameters; and determines the power parameters for the next cycle based on the convergence efficiency parameters and performs tracking and adjustment to maximize the operating efficiency of the charging pile in the next cycle. Thus, under fixed requirements, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the charging pile efficiency range limitation, enabling the equipment to continuously operate in the maximum efficiency range, and reducing overall losses.

[0181] In the case of using integrated units, please refer to Figure 7 , Figure 7 This is a schematic diagram of another charging efficiency optimization device for a charging pile provided in an embodiment of this application, as shown below. Figure 7 As shown, the charging efficiency optimization device 600 for a charging pile includes a processing module 602 and a communication module 601. The processing module 602 controls and manages the operation of the charging efficiency optimization device 600, for example, executing the steps of the acquisition module 610, the first search module 620, the second search module 630, the convergence iteration module 640, and the tracking and control module 650, and / or other processes of the technology described herein. The communication module 601 is used for interaction between the charging efficiency optimization device 600 and other devices. Figure 7 As shown, the charging efficiency optimization device 600 of the charging pile may also include a storage module 603, which is used to store the program code and data of the charging efficiency optimization device 600 of the charging pile.

[0182] The processing module 602 can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 601 can be a transceiver, RF circuitry, or a communication interface, etc. The storage module 603 can be a memory.

[0183] All relevant content in each scenario involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here. The charging efficiency optimization device 600 of the above-mentioned charging pile can perform the above-mentioned... Figure 2 The charging efficiency optimization method for the charging pile shown is illustrated.

[0184] Please see Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device proposed in an embodiment of this application, such as... Figure 8 As shown, the electronic device 800 includes a processor 810, a memory 820, a communication interface 830, and one or more programs 821, which are stored in the memory 820 and configured to be executed by the processor 810.

[0185] The processor 810, memory 820, and communication interface 830 are interconnected and perform communication between them.

[0186] The memory 820 can be a volatile memory such as dynamic random access memory (DRAM) or a non-volatile memory such as a hard disk drive (HDD). The memory 820 stores a set of executable program code, and the processor 810 calls one or more programs 821 stored in the memory 820 to execute some or all of the steps of any of the charging efficiency optimization methods for charging piles described in the above embodiments of the charging pile charging efficiency optimization method.

[0187] Among them, electronic devices 800 may include smartphones (such as Android phones, iOS phones, Windows Phones, etc.), tablet computers, handheld computers, dashcams, in-vehicle electronic devices, servers, laptops, mobile internet electronic devices (MIDs) or wearable electronic devices (such as smartwatches, Bluetooth headsets), etc. The above are just examples and not an exhaustive list, including but not limited to the above electronic devices.

[0188] This application also provides a computer storage medium storing a computer program for electronic data interchange, which causes a computer to perform some or all of the steps of any of the methods described in the above method embodiments, wherein the computer includes an electronic device.

[0189] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may include an electronic device.

[0190] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0191] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0192] In the several embodiments provided in this application, it should be understood that the disclosed apparatus can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical or other forms.

[0193] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0194] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0195] If the integrated units described above are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage device (CMD). Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions to cause a computer electronic device (which may be a personal computer, electronic device, or network electronic device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned memory includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0196] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage device, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, etc.

[0197] The embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for optimizing the charging efficiency of a charging pile, characterized in that, A controller applied to a charging efficiency optimization system, the charging efficiency optimization system further including multiple target charging piles, the method comprising: Acquire multiple power parameters for the current cycle, and determine multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; Based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter, multiple second charging efficiency parameters are determined for the first long search phase. If the second charging efficiency parameter meets the flow conditions of the second step size search stage, multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step size search stage are determined based on the multiple second charging efficiency parameters. Based on the multiple golden section efficiency points and multiple first section point efficiency parameters, iterative calculation operations are performed to determine the convergence efficiency parameters. Based on the convergence efficiency parameters, the power parameters for the next cycle are determined and tracked and adjusted to maximize the operating efficiency of the charging pile in the next cycle.

2. The method according to claim 1, characterized in that, The multiple power parameters of the current cycle include multiple current input power parameters and multiple current output power parameters. Determining multiple first charging efficiency parameters based on the multiple power parameters of the current cycle includes: Multiple current charging efficiencies are determined based on the multiple current input power parameters and the multiple current output power parameters; The power grid frequency period is obtained, which is the operating frequency of the power grid where the target charging pile is located. The attenuation weighting factor is determined based on the power frequency cycle of the power grid. The plurality of first charging efficiency parameters are determined based on the attenuation weighting factor and the plurality of current charging efficiencies, and the plurality of first charging efficiency parameters are associated with the plurality of current input power parameters and / or the plurality of current output power parameters.

3. The method according to claim 1, characterized in that, The determination of multiple second charging efficiency parameters for the first long search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter includes: The first step length is determined based on the first power parameter, and the first search power range is determined based on the plurality of power parameters; A first search operation is performed based on the first step length, the first search power range, and initialization information, wherein the initialization information includes the initial power parameter. The first search operation includes: searching within the first search power range based on the starting power and the first step length to determine the sampling power parameter and the sampling efficiency parameter of the current sampling point, wherein the sampling power parameter of the current sampling point is one of the plurality of power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the plurality of second charging efficiency parameters.

4. The method according to claim 3, characterized in that, The second charging efficiency parameter includes at least one of the sampling efficiency parameter of the next sampling point and the sampling efficiency parameter of the current sampling point. The transition conditions of the second step search phase include: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or the difference between the sampling efficiency parameter of the current sampling point and the sampling efficiency parameter of the next sampling point is greater than a preset efficiency decrease threshold.

5. The method according to claim 4, characterized in that, The determination of the multiple golden section efficiency points and multiple first segmentation point efficiency parameters in the second step size search stage based on the multiple second charging efficiency parameters includes: The second step size is determined based on the first power parameter, and the second search power interval is determined based on the second step size and the sampling power parameter of the current sampling point; Obtain the battery parameters of the vehicle to be charged, and determine the anti-disturbance factor based on the golden ratio parameter and / or the battery parameters of the vehicle to be charged, wherein the golden ratio parameter is the golden ratio and the golden ratio is 0.

618. Based on the anti-disturbance factor, the second search power range, and the golden section parameters, multiple first-type golden sampling division points are determined; Obtain the efficiency parameters of the plurality of first-class gold sampling division points corresponding to the plurality of first-class gold sampling division points.

6. The method according to claim 5, characterized in that, The iterative calculation operation based on the multiple golden ratio efficiency points and the multiple first segmentation point efficiency parameters to determine the convergence efficiency parameters includes: The third search power range and the efficiency parameters of the multiple first segmentation points are determined based on the efficiency parameters of the multiple first segmentation points. Based on the third search power interval and the golden section parameters, determine the fourth search power interval and the efficiency parameters of multiple third division points; The first convergence judgment result is determined based on the efficiency parameter of the second segmentation point, the efficiency parameter of the third segmentation point, and the preset efficiency difference threshold. The second convergence judgment result is determined based on the fourth search power range and the preset range threshold. The convergence efficiency parameter is determined based on the first convergence judgment result and the second convergence judgment result.

7. The method according to claim 1, characterized in that, The step of determining the power parameter for the next cycle based on the convergence efficiency parameter and performing tracking control includes: The power parameters for the next cycle are determined based on the convergence efficiency parameters. The power of the target charging pile is controlled to be the power parameter of the next cycle; Obtain the third charging efficiency parameter and the second power parameter; The efficiency change rate parameter is determined based on the third charging efficiency parameter, the second power parameter, the first charging efficiency parameter, and the first power parameter. Dynamic control decisions are determined based on the efficiency change rate parameter. Tracking and control are performed based on the dynamic control decision and the power parameters of the next cycle.

8. A charging efficiency optimization device for a charging pile, characterized in that, A controller for a charging efficiency optimization system, the charging efficiency optimization system further including multiple target charging piles, the device comprising: An acquisition module is used to acquire multiple power parameters for the current cycle, and to determine multiple first charging efficiency parameters based on the multiple power parameters for the current cycle; The first search module is used to determine multiple second charging efficiency parameters for the first long search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter. The second search module is used to determine multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step length search stage based on the multiple second charging efficiency parameters if the second charging efficiency parameters meet the flow conditions of the second step length search stage. The convergence iteration module is used to perform iterative calculation operations based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine the convergence efficiency parameters. The tracking and control module is used to determine the power parameters for the next cycle based on the convergence efficiency parameters and to perform tracking and control so as to maximize the operating efficiency of the charging pile in the next cycle.

9. A computer-readable storage medium, characterized in that, The charging efficiency optimization program storing the charging pile includes execution instructions, which, when executed by the processor of the electronic device, perform the method as described in any one of claims 1 to 7.

10. An electronic device, characterized in that, It includes a processor, memory, a communication interface, and one or more programs, which are stored in the memory and configured to be executed by the processor; When the processor executes the one or more programs stored in the memory, the processor performs the method as described in any one of claims 1 to 7.

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