Charging efficiency optimization method of charging pile and related device

Abstract

The invention provides a charging efficiency optimization method of a charging pile and a related device, and the method comprises the steps: obtaining a plurality of power parameters of a current period through a controller of a charging efficiency optimization system, and determining a plurality of first charging efficiency parameters based on the plurality of power parameters of the current period; determining a plurality of second charging efficiency parameters of the first step length search stage; determining a plurality of golden section efficiency points and a plurality of first section efficiency parameters of the second step length search stage based on the plurality of second charging efficiency parameters if the second charging efficiency parameters meet the circulation condition of the second step length search stage; determining a convergence efficiency parameter based on the plurality of golden section efficiency points and the plurality of first section point efficiency parameters; and determining a power parameter of the next period based on the convergence efficiency parameter, and performing tracking regulation and control. Therefore, under fixed requirements, real-time fitting of the voltage-power optimal solution can be realized, the efficiency interval limitation of the charging pile is broken through, the equipment continuously operates in the maximum efficiency interval, and the comprehensive loss is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of charging piles, and in particular to a charging efficiency optimization method and related devices for a charging pile. Background Art

[0002] As the number of new energy vehicles continues to rise, the current industry focus is on "power competition" and "density improvement." For example, the industry has increased charging speed and network density through megawatt-level supercharging. However, the problem of a narrow efficiency range has not been fundamentally solved: the peak efficiency of charging piles is only achieved in a narrow voltage / power range, while the actual charging range of users covers a wider range, resulting in a significant reduction in average efficiency; when charging at high power, thermal management energy consumption surges; to ensure high compatibility, operators need to configure wide-voltage range modules, but the efficiency of traditional topologies drops sharply under non-standard operating conditions, resulting in energy waste on the grid side and electricity premiums on the user side. In summary, under complex operating conditions with a wide power range, variable ambient temperature, and multiple voltage requirements, there are still problems such as insufficient global efficiency of the charging system, unstable high-power output, and high overall losses. Summary of the Invention

[0003] The embodiments of the present application provide a charging efficiency optimization method and related devices for a charging pile, which can break through the efficiency range limit of the charging pile, enable the equipment to continuously operate in the maximum efficiency range, and reduce comprehensive losses.

[0004] In a first aspect, an embodiment of the present application provides a method for optimizing the charging efficiency of a charging pile, which is applied to a controller of a charging efficiency optimization system, wherein the charging efficiency optimization system further includes multiple target charging piles. The method includes: Acquiring a plurality of power parameters of a current cycle, and determining a plurality of first charging efficiency parameters based on the plurality of power parameters of the current cycle; Determining a plurality of second charging efficiency parameters in a first length search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of efficiency thresholds, and the first power parameter; If the second charging efficiency parameter meets the flow condition of the second step search phase, determining a plurality of golden section efficiency points and a plurality of first section point efficiency parameters of the second step search phase based on the plurality of second charging efficiency parameters; Performing an iterative calculation operation based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine a convergence efficiency parameter; The power parameters of the next cycle are determined based on the convergence efficiency parameters and tracked and regulated to maximize the operating efficiency of the charging pile in the next cycle.

[0005] In a 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; Obtaining a power grid power frequency cycle, where the power grid power frequency cycle is the operating frequency of the power grid where the target charging pile is located; Determining an attenuation weight factor based on the power frequency cycle of the power grid; The multiple first charging efficiency parameters are determined based on the attenuation weight factor and the multiple current charging efficiencies, and the multiple first charging efficiency parameters are correspondingly associated with the multiple current input power parameters and / or the multiple current output power parameters.

[0006] In a possible embodiment, determining a plurality of second charging efficiency parameters in the first step length search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of 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 interval based on the multiple power parameters; performing a first search operation based on the first step length, the first search power interval, and initialization information, the initialization information including a starting power parameter; The first search operation includes: searching in the first search power interval based on the starting power and the first step length, and determining a sampling power parameter and a sampling efficiency parameter of a current sampling point, wherein the sampling power parameter of the current sampling point is one of the multiple power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the multiple second charging efficiency parameters.

[0007] In a possible embodiment, the second charging efficiency parameter includes at least one of a sampling efficiency parameter of a next sampling point and a sampling efficiency parameter of the current sampling point. The transition condition of the second step search phase includes: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or a difference parameter 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 degradation threshold.

[0008] In a possible embodiment, determining a plurality of golden section efficiency points and a plurality of first section point efficiency parameters in the second step search phase based on the plurality of second charging efficiency parameters includes: determining a second step size based on the first power parameter, and determining a second search power interval based on the second step size and the sampling power parameter of the current sampling point; Obtaining 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 a plurality of first-type golden sampling segmentation points based on the anti-disturbance factor, the second search power interval, and the golden segmentation parameter; The plurality of first segmentation point efficiency parameters corresponding to the plurality of first-type golden sampling segmentation points are obtained.

[0009] In a possible embodiment, performing an iterative calculation operation based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine the convergence efficiency parameter includes: determining a third search power interval and a plurality of second split point efficiency parameters based on the plurality of first split point efficiency parameters; determining a fourth search power interval and a plurality of third segmentation point efficiency parameters based on the third search power interval and the golden section parameter; determining a first convergence judgment result based on the second segmentation point efficiency parameter, the third segmentation point efficiency parameter, and a preset efficiency difference threshold; Determining a second convergence judgment result based on the fourth search power interval and a preset interval threshold; A convergence efficiency parameter is determined based on the first convergence judgment result and the second convergence judgment result.

[0010] In a possible embodiment, determining the power parameter of the next cycle based on the convergence efficiency parameter and performing tracking and regulation includes: Determining a 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 of the next cycle; Obtaining a third charging efficiency parameter and a second power parameter; determining an efficiency change rate parameter based on a 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; Tracking and regulation are performed based on the dynamic regulation decision and the power parameters of the next cycle.

[0011] In a second aspect, an embodiment of the present application provides a charging efficiency optimization device for a charging pile, which is 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: an acquisition module, configured to acquire a plurality of power parameters of a current cycle, and determine a plurality of first charging efficiency parameters based on the plurality of power parameters of the current cycle; a first search module, configured to determine a plurality of second charging efficiency parameters in a first long search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of efficiency thresholds, and the first power parameter; a second search module, configured to determine, if the second charging efficiency parameter meets the flow condition of the second step search phase, a plurality of golden section efficiency points and a plurality of first section point efficiency parameters of the second step search phase based on the plurality of second charging efficiency parameters; a convergence iteration module, configured to perform an iterative calculation operation based on the plurality of golden section efficiency points and the plurality of first section point efficiency parameters to determine a convergence efficiency parameter; The tracking and control module is used to determine the power parameters of the next cycle based on the convergence efficiency parameters and perform tracking and control to maximize the operating efficiency of the charging pile in the next cycle.

[0012] In a third aspect, an embodiment of the present application provides a computer-readable storage medium on which a charging efficiency optimization program for a charging pile is stored. The charging efficiency optimization program for the charging pile includes execution instructions. When the processor executes the execution instructions stored in the memory, the processor executes some or all of the steps described in the first aspect.

[0013] In a fourth aspect, an embodiment of the present application provides an electronic device comprising 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. When the processor executes the one or more programs, the processor executes instructions of some or all of the steps described in the first aspect of the embodiment of the present application.

[0014] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0015] By implementing the embodiments of the present application, the controller of the charging efficiency optimization system obtains multiple power parameters for the current cycle, 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 search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step search phase based on the multiple second charging efficiency parameters if the second charging efficiency parameters meet the flow conditions for the second step search phase, performs iterative calculations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine a convergence efficiency parameter, and determines the power parameters for the next cycle based on the convergence efficiency parameter and performs tracking and control to maximize the charging pile operating efficiency for the next cycle. In this way, under fixed demand, a real-time fitting of the voltage-power optimal solution can be achieved, breaking through the efficiency range limit of the charging pile, allowing the equipment to continuously operate in the maximum efficiency range, and reducing overall losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the background technology, the drawings required for use in the embodiments of the present invention or the background technology will be described below.

[0017] Figure 1 This is a schematic diagram of the architecture of a charging efficiency optimization system provided in an embodiment of the present application; Figure 2 This is a flow chart of a method for optimizing the charging efficiency of a charging pile provided in an embodiment of the present application; Figure 3 This is a schematic diagram of power parameter acquisition of a charging efficiency optimization method for a charging pile provided in an embodiment of the present application; Figure 4 This is a flow chart of determining a convergence efficiency parameter of a charging efficiency optimization method for a charging pile provided in an embodiment of the present application; Figure 5 1 is a flow chart of another method for optimizing the charging efficiency of a charging pile proposed in an embodiment of the present application; Figure 6 This is a structural diagram of a charging efficiency optimization device for a charging pile provided in an embodiment of the present application; Figure 7 This is a schematic structural diagram of another charging efficiency optimization device for a charging pile provided in an embodiment of the present application; Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a specific order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or electronic device comprising a series of steps or units is not limited to the listed steps or units, but may, in an optional example, also include steps or units not listed, or may, in an optional example, include other steps or units inherent to the process, method, product, or electronic device.

[0020] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] As the number of new energy vehicles continues to rise, the current industry focus is on "power competition" and "density improvement." For example, the industry has increased charging speed and network density through megawatt-level supercharging. However, the problem of a narrow efficiency range has not been fundamentally solved: the peak efficiency of charging piles is only achieved in a narrow voltage / power range, while the actual charging range of users covers a wider range, resulting in a significant reduction in average efficiency; when charging at high power, thermal management energy consumption surges; to ensure high compatibility, operators need to configure wide-voltage range modules, but the efficiency of traditional topologies drops sharply under non-standard operating conditions, resulting in energy waste on the grid side and electricity premiums on the user side. In summary, under complex operating conditions with a wide power range, variable ambient temperature, and multiple voltage requirements, there are still problems such as insufficient global efficiency of the charging system, unstable high-power output, and high overall losses.

[0022] In response to the above problems, the embodiments of the present application provide a charging efficiency optimization method and related devices for a charging pile, which can achieve real-time fitting of the voltage-power optimal solution under fixed requirements, break through the efficiency range limit of the charging pile, enable the equipment to continuously operate in the maximum efficiency range, and reduce comprehensive losses.

[0023] The charging efficiency optimization method and related device of the charging pile provided in the embodiment of the present application can be applied to Figure 1 In the charging efficiency optimization system shown, see Figure 1 , Figure 1 1 is a schematic diagram of the architecture of a charging efficiency optimization system provided in an embodiment of the present 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.

[0024] 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 be provided with a user interface so that the user can easily input the specified charging requirements. In some cases, the target charging pile 120 may also be a simulated charging pile, which is essentially equivalent to the charging pile actually in use and is used for simulation training. The controller 110 refers to a computer or single-chip microcomputer used to process a large number of computing tasks and store data. In this solution, a charging efficiency optimization program is deployed on the controller 110 for the charging efficiency optimization method 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 of the charging pile.

[0025] Based on this, the present application provides a charging efficiency optimization method and related devices for a charging pile, and the present application is described in detail below with reference to the accompanying drawings.

[0026] See also Figure 2 , Figure 2 This is a flow chart of a method for optimizing the charging efficiency of a charging pile provided in an embodiment of the present application. The method is applied to a controller of a charging efficiency optimization system. The charging efficiency optimization system further includes a plurality of target charging piles, such as Figure 2 As shown, the method includes the following steps: S210: Acquire multiple power parameters of a current cycle, and determine multiple first charging efficiency parameters based on the multiple power parameters of the current cycle.

[0027] The current cycle can be a pre-set period of time, and the multiple power parameters include the output power collected based on the target charging pile output voltage. The target charging pile output voltage changes dynamically. Within the preset output voltage range, in the current cycle, the voltage is regulated and output in a step-by-step manner. In some cases, each voltage regulation is maintained for a preset time length, and multiple output powers at the corresponding voltage within 2 minutes are obtained. The preset time length can be 2 minutes or other time lengths, which are selected according to actual applications and are not limited here. The multiple power parameters can include at least one of an input power parameter and an output power parameter.

[0028] The first charging efficiency parameter may be determined by a plurality of power parameters, and specifically may be determined based on a ratio between an output power parameter and an input power parameter.

[0029] Optionally, the multiple power parameters may be directly acquired or calculated based on at least two of the acquired input voltage, output voltage, input current, and output current. The output power parameter may be determined by multiplying the output voltage and the output current, and the input power parameter may be determined by multiplying the input voltage and the input current.

[0030] In a possible embodiment, the multiple power parameters of the current cycle include multiple current input power parameters and multiple current output power parameters, and the determining of multiple first charging efficiency parameters based on the multiple power parameters of the current cycle 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 power frequency cycle, which is the operating frequency of the power grid where the target charging pile is located; determining an attenuation weight factor based on the power grid power frequency cycle; determining the multiple first charging efficiency parameters based on the attenuation weight factor and the multiple current charging efficiencies, and the multiple first charging efficiency parameters are correspondingly associated with the multiple current input power parameters and / or the multiple current output power parameters.

[0031] The multiple current input power parameters include the input power parameter of the target charging station and the output power parameter of the target charging station to the outside world (the vehicle to be charged). Optionally, multiple current charging efficiencies can be determined by dividing the output power parameter by the input power parameter. A charging efficiency-output power characteristic curve can be constructed, replacing traditional voltage / current control and achieving a shift from a "power-oriented" to an "efficiency-oriented" approach.

[0032] After determining the current charging efficiency, a 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 associated with the power grid power frequency cycle. The power grid power frequency refers to the nominal AC power frequency during normal operation of the power system, and the power grid power frequency cycle is the time required for a complete AC power waveform corresponding to the power frequency.

[0033] Optionally, determining the attenuation weight factor based on the power frequency cycle of the power grid can be achieved by the following method: determining a half-cycle based on the power frequency cycle of the power grid, where the value of the half-cycle matches the harmonic interference characteristics, accurately corresponds to the full cycle of the second harmonic (twice the length of the power frequency cycle of the power grid), and is closely related to the symmetry characteristics of common odd harmonics within the half-cycle; determining the attenuation coefficient based on Monte Carlo simulation experiments; and determining the attenuation weight factor by performing an exponential operation based on the attenuation coefficient and the half-cycle. Optionally, the above-mentioned exponential operation based on the attenuation coefficient and the half-cycle to determine the attenuation weight factor can be: ,in, is the attenuation coefficient, For half a period, is the attenuation weight factor. Optionally, the attenuation coefficient may be determined by Monte Carlo simulation at a given time step Δt. It is the optimal value or effective value in this specific application scenario.

[0034] For example, the power grid frequency period is 50Hz / 20ms, based on which the half period is determined. The attenuation coefficient is determined based on Monte Carlo simulation. In some possible cases, the attenuation coefficient may also be pre-calculated based on pre-set grid-related parameters and may be directly called, which is not limited here.

[0035] Optionally, determining the multiple first charging efficiency parameters based on the attenuation weight factor and the multiple current charging efficiencies can be achieved by performing a weighted average of the current charging efficiencies of the time series data. This can be achieved by the following formula: ; in, is the first charging efficiency parameter mentioned above.

[0036] For examples, see Figure 3 , Figure 3 This is a schematic diagram of power parameter acquisition of a charging efficiency optimization method for a charging pile provided in an embodiment of the present application, such as Figure 3 As shown, in the current cycle, the target charging pile is controlled to perform step-by-step voltage regulation within the output voltage range (300V-1000V), for example, 400V, 700V, and 1000V. Each voltage regulation point maintains 400V, 700V, and 1000V for 2 minutes of stable operation, and the power parameters corresponding to each voltage regulation point within 2 minutes are obtained as multiple power parameters in the current cycle.

[0037] It can be seen that in this embodiment, by adding the attenuation weight factor, the reliability of the data is improved and efficient dynamic response is achieved. Determining the attenuation weight factor based on the power grid frequency cycle can accurately match the power grid, improve scenario adaptability and measurement accuracy.

[0038] S220 : Determine a plurality of second charging efficiency parameters of a first step length search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of efficiency thresholds, and the first power parameter.

[0039] Among them, the first step long search stage is a large step search stage, which is used to preliminarily and quickly locate the peak interval; in the first step long search stage, a traversal search is performed, and a preliminary large range is determined based on multiple power parameters, multiple efficiency thresholds, the first power parameter, and the first charging efficiency parameter, and the second charging efficiency parameter is associated with the boundary of the preliminary large range.

[0040] The multiple efficiency thresholds may be pre-set and used for comparison with the first charging efficiency parameter or with the mathematical calculation results of the multiple first charging efficiency parameters to determine whether the first search step ends and whether to proceed to the next search step. The first power parameter may be a rated power parameter, which is associated with the step size searched in the first search step.

[0041] In a possible embodiment, determining multiple second charging efficiency parameters in the first step length search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter includes: determining the first step length based on the first power parameter, and determining a first search power interval based on the multiple power parameters; performing a first search operation based on the first step length, the first search power interval, and initialization information, the initialization information including a starting power parameter; the first search operation includes: searching in the first search power interval based on the starting power and the first step length to determine a sampling power parameter and a sampling efficiency parameter of the current sampling point, the sampling power parameter of the current sampling point being one of the multiple power parameters, and the sampling efficiency parameter of the current sampling point being at least one of the multiple second charging efficiency parameters.

[0042] The first step length is the step length used for traversal search in the first step 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, for example, the first step length is p×P rated , the above p is the preset coefficient, P rated The first power parameter may be the rated power. For example, p may be 5%, i.e., the first step length is 5% of the rated power. In the first step length search phase, an initial search interval, i.e., a first search power interval, needs to be defined. The first search power interval may be determined based on multiple power parameters. For example, the first power search range may be determined based on the maximum and minimum values ​​of the multiple power parameters. In some possible cases, the range may be dynamically adjusted based on a preset search range and multiple power parameters to further determine the first power search range.

[0043] The initialization information includes a starting power parameter, which is the starting point of the search. The first step length is increased from the starting power parameter to perform the first step length search. For example, the starting power parameter may be 0.

[0044] After determining the starting 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 starting 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 the current sampling power parameter is measured or acquired. The sampling efficiency parameter and the sampling power parameter correspond to one of the plurality of second charging efficiency parameters, and the sampling power parameter corresponds to one of the plurality of first power parameters. The data pair of the sampling power parameter and the sampling efficiency parameter is stored to provide a basis for subsequent peak determination.

[0045] For example, determine the first step length as ΔP=5%P rated , determine the first search power interval as [P low , P high ], the starting power parameter is 0, and it starts to increase at ΔP from 0. The sampling power parameter of the next sampling point is P prev =P curr +ΔP, where P curr is the sampling power parameter of the current sampling point. When searching for the first step, P curr = Starting power parameter = 0. The sampling power parameter at the next sampling point is used to obtain or collect the corresponding sampling efficiency parameter η curr Storage (P curr , η curr ) data pair.

[0046] It can be seen that in this embodiment, η is used as the direct feedback variable, and a large step size is used to quickly locate in the first step search interval, which narrows the range for subsequent fine tracking, effectively improving the peak positioning speed, and then improving the peak positioning speed to quickly and accurately determine the maximum charging efficiency.

[0047] S230: If the second charging efficiency parameter meets the flow condition of the second step search phase, determine a plurality of golden section efficiency points and a plurality of first section point efficiency parameters of the second step search phase based on the plurality of second charging efficiency parameters.

[0048] Among them, the second charging efficiency parameter is the sampling efficiency parameter obtained in the first step search stage. Based on the second charging efficiency parameter, it is determined whether the second step search stage can be entered. The flow condition of the second step search stage is a preset adjustment for constraining whether the second step search can be entered, which may specifically include the constraint condition for the second charging efficiency parameter.

[0049] If the second charging efficiency parameter meets the flow condition of the second step search phase, the second step search phase is entered to perform the second step search traversal.

[0050] Optionally, if the second charging efficiency parameter does not meet the flow condition of the second step search phase, the first step search operation is performed in a loop, the sampling power parameter is increased by the first step, and the sampling efficiency parameter of the next sampling point is obtained.

[0051] Among them, in the second step search stage, a sampling method of golden section efficiency points based on the golden section parameters is used to determine multiple golden section efficiency points and first section point efficiency parameters. The above-mentioned golden section efficiency points are interval-related parameters, which can be understood as interval boundary parameters. The first section point efficiency parameter is the efficiency peak point.

[0052] In a possible embodiment, the second charging efficiency parameter includes at least one of a sampling efficiency parameter of a next sampling point and a sampling efficiency parameter of the current sampling point. The transition condition of the second step search phase includes: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or a difference parameter 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 degradation threshold.

[0053] Among them, if it is detected that the sampling efficiency parameter of the current sampling point meets η curr >η thr When (where η thr is the preset efficiency threshold), it can be determined that the system has reached the target efficiency range, triggering the flow to the second step search phase; when the efficiency attenuation between adjacent sampling points satisfies |η curr –η prev |>Δη thr (Δη thr is the preset efficiency drop threshold), and η curr <η prev , it is determined that the efficiency has entered a rapid decline range, and the current first step search phase is terminated in advance and the second step search phase is transferred. thr Preset, example, η thr It can be 90%, Δη thr It can be 2%. If any of the above two conditions is met, the flow condition of the second compensation search phase is met. If the above two conditions are not met, enter the next cycle and let η prev =η curr , continue to cycle the first long search phase until the flow condition of the second long search phase is met.

[0054] For example, the default value is η thr =90%, Δη thr=2%, in obtaining (P curr , η curr ) data pairs and η prev After that, based on η curr ,η prev and Δη thr Compare. Condition 1: If η curr >η thr =90%, indicating that it has entered the high-efficiency zone and needs to switch to micro-step fine search, that is, the second step search stage; Condition 2: If the efficiency difference between two adjacent times |η curr -η prev ∣>Δη thr =2%, and η curr <η prev , indicating that the efficiency begins to decline significantly and may be close to the peak area, triggering the micro-step switching, and it is necessary to switch to the micro-step fine search, that is, the second step search stage; if one of conditions 1 or 2 is not met, enter the next cycle, let η prev =η curr , continue to cycle the first long search phase until the flow condition of the second long search phase is met.

[0055] It can be seen that in this embodiment, by setting two conditional boundaries, the range for entering the second step search phase 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.

[0056] In a possible embodiment, determining multiple golden section efficiency points and multiple first segmentation point efficiency parameters in the second step search phase based on the multiple second charging efficiency parameters includes: determining a second step based on the first power parameter, and determining a second search power interval based on the second step and the sampling power parameter of the current sampling point; obtaining battery parameters of the vehicle to be charged, and determining an anti-disturbance factor based on the golden section parameters and / or the battery parameters of the vehicle to be charged; determining multiple first-class golden sampling segmentation points based on the anti-disturbance factor, the second search power interval, and the golden section parameters; and obtaining the multiple first segmentation point efficiency parameters corresponding to the multiple first-class golden sampling segmentation points.

[0057] The second step length is the step length for traversal search in the second step length search phase, which can be determined by the first power parameter. For example, the second step length can be calculated based on the first power parameter, for example, the second step length is q×P rated , the above q is the preset coefficient, P ratedThe first power parameter may be a rated power. For example, q may be 0.5%, i.e., the first step length is 0.5% of the rated power. During the second step length search phase, a search interval, i.e., a second search power interval, needs to be defined. The second search power interval may be determined based on the sampled power parameter of the current sampling point. For example, the second power search range may be determined by extending a certain range in the positive and negative directions from the sampled power parameter of the current sampling point as the center. In some possible cases, the range may be expanded based on a golden section parameter to further determine the second power search range.

[0058] Optionally, the left edge P of the second power search interval low and the right boundary P high It can be determined by the following process: the left boundary P low Equal to the sampling power parameter of the current sampling point minus N times q×P rated , right boundary P high Equal to 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 ].

[0059] Among them, the anti-disturbance factor is used to prevent local extreme value collapse and suppress temperature fluctuations. The disturbance factor can be determined based on the golden section parameter and the battery parameters of the vehicle to be charged by the target charging pile. The battery parameters include the thermal time constant. Optionally, the parameters of the battery of the vehicle being charged by the target charging pile can be preset or obtained at the beginning of charging. For example, if it is preset, it can be a preset thermal time constant of at least one typical power battery, such as a preset thermal time constant τ≈100s; if it is at the beginning of charging, the vehicle information of the vehicle to be charged is obtained and the thermal time constant of the vehicle to be charged is directly obtained, or the vehicle model is matched to the corresponding thermal time constant in the database.

[0060] 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, where the disturbance amplitude A 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.

[0061] Specifically, the anti-disturbance factor β=0.618+0.05•sin(2πt / T), where 0.618 is the golden section parameter and 0.05 is the disturbance amplitude A.

[0062] For example, if the thermal time constant is τ≈100s, the disturbance period T=0.1τ≈10s, and the disturbance amplitude A=0.05.

[0063] The plurality of first-class golden sampling segmentation points include the first golden sampling segmentation point and the second golden sampling segmentation point, and the second search power interval includes the left boundary P low and the right boundary P high , based on the anti-disturbance factor, the left boundary P of the second search power interval low and the right boundary P high The steps of determining a plurality of first-class golden sampling segmentation points based on the right boundary P and the golden segmentation parameters may include: high Subtract the anti-disturbance factor and the left boundary P low and the right boundary P high The product of the difference determines the first golden sampling segmentation point, based on the left boundary P low Add the anti-disturbance factor and the left boundary P low and the right boundary P high The product of the difference determines the second golden sampling segmentation point. Example: the first golden sampling segmentation point a=P high -β×(P high -P low ), the second golden sampling point b=P low +β×(P high -P low ).

[0064] Wherein, based on the multiple first-type golden sampling segmentation points (the first golden sampling segmentation point and the second golden sampling segmentation point) obtained above, the corresponding multiple first segmentation point efficiency parameters are detected or obtained, and the first segmentation point efficiency parameters include the efficiency value η corresponding to the first golden sampling segmentation point. a The second segmentation point efficiency parameter includes the efficiency value η corresponding to the second golden sampling segmentation point b .

[0065] For example, the second search power interval is [296.4, 303.6], then a=303.6-0.618β×7.2, b=296.4+0.618β×7.2.

[0066] It can be seen that in this embodiment, the mathematical optimization algorithm (golden section method) is deeply integrated with engineering physics constraints (battery thermodynamics). Through the periodic anti-disturbance weight factor and stable measurement strategy, a safe and efficient parameter search is achieved, which effectively improves the peak positioning speed and accuracy, thereby improving the peak positioning speed and accuracy, so as to quickly and accurately determine the maximum charging efficiency.

[0067] In a possible embodiment, determining multiple golden section efficiency points and multiple first segmentation point efficiency parameters in the second step search phase based on the multiple second charging efficiency parameters includes: determining a second step based on the first power parameter, and determining a second search power interval based on the second step and the sampling power parameter of the current sampling point; determining multiple first-class golden sampling segmentation points based on the second search power interval and the golden section parameter; and obtaining the multiple first segmentation point efficiency parameters corresponding to the multiple first-class golden sampling segmentation points.

[0068] The second step length is the step length for traversal search in the second step length search phase, which can be determined by the first power parameter. For example, the second step length can be calculated based on the first power parameter, for example, the second step length is q×P rated , the above q is the preset coefficient, P rated The first power parameter may be a rated power. For example, q may be 0.5%, i.e., the first step length is 0.5% of the rated power. During the second step length search phase, a search interval, i.e., a second search power interval, needs to be defined. The second search power interval may be determined based on the sampled power parameter of the current sampling point. For example, the second power search range may be determined by extending a certain range in the positive and negative directions from the sampled power parameter of the current sampling point as the center. In some possible cases, the range may be expanded based on a golden section parameter to further determine the second power search range.

[0069] Optionally, the left edge P of the second power search interval low and the right boundary P high It can be determined by the following process: the left boundary P low Equal to the sampling power parameter of the current sampling point minus N times q×P rated , right boundary P high Equal to 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 , Pcurr +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 ].

[0070] The plurality of first-class golden sampling segmentation points include the first golden sampling segmentation point and the second golden sampling segmentation point, and the second search power interval includes the left boundary P low and the right boundary P high , based on the left boundary P of the second search power interval low and the right boundary P high The steps of determining a plurality of first-class golden sampling segmentation points based on the right boundary P and the golden segmentation parameters may include: high Subtract the left edge P low and the right boundary P high The difference between the left and right edges determines the first golden sampling point. low Plus the left boundary P low and the right boundary P high The difference between the two determines the second golden sampling segmentation point. Example: the first golden sampling segmentation point a=P high -(P high -P low ), the second golden sampling point b=P low +(P high -P low ).

[0071] Wherein, based on the multiple first-type golden sampling segmentation points (the first golden sampling segmentation point and the second golden sampling segmentation point) obtained above, the corresponding multiple first segmentation point efficiency parameters are detected or obtained, and the first segmentation point efficiency parameters include the efficiency value η corresponding to the first golden sampling segmentation point. a The second segmentation point efficiency parameter includes the efficiency value η corresponding to the second golden sampling segmentation point b .

[0072] For example, the second search power interval is [296.4, 303.6], a=303.6-0.618×7.2≈299.1kW, b=296.4+0.618×7.2≈300.9kW.

[0073] It can be seen that in this embodiment, based on the mathematical optimization algorithm (golden section method), a periodic anti-disturbance weight factor and a stable measurement strategy are used to achieve safe and efficient parameter search, effectively improve the peak positioning speed and accuracy, and then improve the peak positioning speed and accuracy to quickly and accurately determine the maximum charging efficiency.

[0074] S240 , performing an iterative calculation operation based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine a convergence efficiency parameter.

[0075] Among them, after determining the efficiency parameters of multiple segmentation points, it is necessary to further determine whether the interval of the golden section point efficiency parameter 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 convergence peak value, that is, the convergence efficiency parameter.

[0076] In one possible embodiment, see Figure 4 , Figure 4 This is a flow chart of determining convergence efficiency parameters of a charging efficiency optimization method for a charging pile provided in an embodiment of the present application, such as Figure 4 As shown, the iterative calculation operation based on the multiple golden section efficiency points and the multiple first segmentation point efficiency parameters to determine the convergence efficiency parameter includes: S410: Determine a third search power interval and a plurality of second division point efficiency parameters based on the plurality of first division point efficiency parameters.

[0077] S420: Determine a fourth search power interval and a plurality of third segmentation point efficiency parameters based on the third search power interval and the golden section parameter.

[0078] The iterating based on the third search interval and further based on the golden section parameter is performed. Optionally, the iterating based on the third search interval and further based on the golden section parameter is performed to determine the fourth search power interval and multiple third segmentation point efficiency parameters. This may include: S421, obtaining 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; S422, determining a plurality of second-type golden sampling segmentation points based on the third search power interval, the golden segmentation parameter, and / or the anti-disturbance factor; S423, obtaining the plurality of third segmentation point efficiency parameters and the fourth search power interval corresponding to the plurality of second-type golden sampling segmentation points; The third golden sampling segmentation point is determined based on the right boundary minus the difference between the left boundary and the right boundary, and the fourth golden sampling segmentation point is determined based on the left boundary plus the difference between the left boundary and the right boundary.

[0079] Wherein, based on the multiple second-type golden sampling segmentation points (the third golden sampling segmentation point and the fourth golden sampling segmentation point) obtained above, the corresponding multiple third segmentation point efficiency parameters are detected or obtained, and the first segmentation point efficiency parameter includes the efficiency value η corresponding to the third golden sampling segmentation point. c The fourth segmentation point efficiency parameter includes the efficiency value η corresponding to the fourth golden sampling segmentation point. d .

[0080] The contents of the above steps are similar to the embodiment included in step S230. Please refer to the contents of the above embodiment and will not be repeated here.

[0081] S430: Determine a first convergence judgment result based on the second segmentation point efficiency parameter, the third segmentation point efficiency parameter, and a preset efficiency difference threshold.

[0082] S440: Determine a second convergence judgment result based on the fourth search power interval and a preset interval threshold; and determine a convergence efficiency parameter based on the first convergence judgment result and the second convergence judgment result.

[0083] The third search power interval is based on the left and right boundaries of the second search power interval, and the first segmentation point efficiency parameter includes the efficiency value η corresponding to the first golden sampling segmentation point. a The second segmentation point efficiency parameter includes the efficiency value η corresponding to the second golden sampling segmentation point b Further determine the boundary value.

[0084] Optionally, determining the third search power interval and the plurality of second split point efficiency parameters based on the plurality of first split point efficiency parameters includes: if η a >η b , indicating that the peak is in the left half of the interval, and the third search power interval is determined to be [P low ,b]; if η b ≥η a , indicating that the peak is in the right half of the interval, and the third search power interval is determined to be [a,P high ].

[0085] Determining the first convergence judgment result based on the second segmentation point efficiency parameter, the third segmentation point efficiency parameter, and a preset efficiency difference threshold includes: determining a relationship between the efficiency difference between two adjacent measurements (the second segmentation point efficiency parameter and the third segmentation point efficiency parameter) and the preset efficiency difference threshold, specifically, |η new -η old |<ε, indicating that the efficiency is close to the peak, where ε is the preset efficiency difference threshold, preferably, ε=0.1%; if |η is satisfied new -η old∣<ε, the first convergence judgment result is determined to be the first result. If ∣η is not satisfied new -η old |<ε, then the first convergence judgment result is determined to be the second result.

[0086] Wherein, the second convergence judgment result is determined based on the fourth search power interval and the preset interval threshold, including: P in the fourth search power interval high -P low <ξ, where ξ is the preset interval threshold, ξ=2×q×P rated =2×0.5%×P rated , indicating that the power adjustment accuracy has reached the micro-step level; if P high -P low <ξ, then the second convergence judgment result is determined to be the third result. If it does not meet P high -P low <ξ, then the second convergence judgment result is determined to be the fourth result.

[0087] Among them, the first convergence judgment result is the first result, or the second convergence judgment result is the third result. If either one is satisfied, it is determined that the convergence condition is reached at this time, the iterative calculation operation is terminated, and the current interval is locked to determine the convergence efficiency parameter.

[0088] Determining the convergence efficiency parameter includes: locking the midpoint of the current interval as the maximum efficiency point convergence efficiency parameter η max , and determine the corresponding power P at this time opt .

[0089] It should be noted that the above iterations can be repeated. In the embodiment, only the process of iterating twice is taken as an example. If the first convergence judgment result and the second convergence judgment result do not meet the convergence requirements, the iteration is continued based on this until the first convergence judgment result is the first result within the preset iteration number threshold, or the second convergence judgment result is the third result, either of which is met.

[0090] It can be seen that in this embodiment, through the combination of two-level interval contraction and dual-dimensional convergence judgment, the optimal efficiency point available for engineering is efficiently output while ensuring safe thermal management of the power battery, and the micro-step golden section multi-level iterative fine tracking is performed to improve the peak positioning speed.

[0091] In a possible embodiment, if the iterative calculation operation fails to converge after exceeding a preset iteration number threshold (preferably 10 times), the fault diagnosis module is triggered and the system returns to a conventional charging parameter point (such as the smaller value of the rated power and the required voltage / current).

[0092] S250: Determine the power parameter of the next cycle based on the convergence efficiency parameter and perform tracking and regulation to maximize the operating efficiency of the charging pile in the next cycle.

[0093] After the convergence efficiency parameter is determined, the convergence efficiency parameter can be directly used as a subsequent power parameter to charge the vehicle to be charged, and cyclic optimization can be performed based on the convergence efficiency parameter in the next cycle.

[0094] In a possible embodiment, determining the power parameter of the next cycle based on the convergence efficiency parameter and performing tracking and regulation include: determining the power parameter of the next cycle based on the convergence efficiency parameter; controlling the power of the target charging pile to be the power parameter of 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 and regulation based on the dynamic control decision and the power parameter of the next cycle.

[0095] After determining the maximum efficiency point (convergence efficiency parameter), dynamic maintenance and fine-tuning are achieved through real-time monitoring of the efficiency change rate (dη / dP), entering the "tracking and maintenance" phase. The convergence efficiency parameter can be used as output to obtain the parameters for the next cycle, namely the third charging efficiency parameter and the second power parameter. A derivative calculation is then performed based on the third charging efficiency parameter and the second power parameter, the first charging efficiency parameter, and the first power parameter to determine the efficiency change rate parameter. A dynamic control decision is determined based on this efficiency change rate parameter. The dynamic control decision can include at least one of maintaining the current power, increasing the output power, or decreasing the output power. Tracking control is then performed based on the power parameters for the next cycle based on the dynamic control decision.

[0096] Optionally, the efficiency change rate parameter is determined by performing a derivative calculation based on the third charging efficiency parameter and the second power parameter, the first charging efficiency parameter, and the first power parameter, including: efficiency change rate parameter dη / dP=(η k –η k-1 ) / (P k –P k-1 ), where η k is the third charging efficiency parameter, P k is the second power parameter, η k-1 is the first charging efficiency parameter, P k-1 is the first power parameter.

[0097] Optionally, determining a dynamic control decision based on the efficiency change rate parameter includes: if |dη / dP| < a first change rate threshold, then the dynamic control decision is to maintain the current power, and the first change rate threshold may preferably be 0.1%; if dη / dP > a second change rate threshold, then the dynamic control decision is to increase the output power, and the second change rate threshold may preferably be 0.3%; and if dη / dP < - the second change rate threshold, then the dynamic control decision is to decrease the output power.

[0098] Optionally, the specific values ​​for increasing the output power and decreasing the output power may be preset or determined by an algorithm, which is not limited here.

[0099] It can be seen that in this embodiment, by performing dynamic fine-tuning after charging at a determined maximum efficiency value, it is possible to achieve real-time fitting of the voltage-power optimal solution, break through the efficiency range limit of the charging pile, enable the equipment to continuously operate in the maximum efficiency range, and reduce comprehensive losses.

[0100] For example, please refer to Figure 3 , insert the terminal charging gun into the vehicle simulator (target charging vehicle), start charging, and set the BMS required voltage and current to the maximum value within the charging stack capacity range (1000V, 400A); control the target charging pile to limit the output voltage range (300V-1000V) and perform step-by-step voltage regulation within the output voltage range, for example, 400V, 700V, 1000V, and each voltage regulation point maintains 400V, 700V, and 1000V for 2 minutes of stable operation, and obtain the power parameters corresponding to each voltage regulation point within 2 minutes as multiple power parameters in the current cycle to generate η-P out curve; the system starts the variable step-size search algorithm, with an initial large step size of 18kW (5%P_rated) to quickly locate the 90% efficiency area (the first search power interval); after entering the 90% efficiency area, it switches to a micro-step size of 1.8kW to determine the second search power interval, and iterates three times through the golden section method to lock the peak efficiency point (1000V / 78.8kW, efficiency 95.8%). The whole process takes 1.2 seconds; during charging, the efficiency change rate (dη / dP) is monitored in real time, and the maximum efficiency tracking algorithm is used to dynamically maintain the maximum efficiency output of the charging stack, entering the continuous "tracking and maintaining" maximum output efficiency stage.

[0101] As can be seen, in this embodiment, the controller of the charging efficiency optimization system obtains multiple power parameters for the current cycle and determines multiple first charging efficiency parameters based on the multiple power parameters of the current cycle; determines multiple second charging efficiency parameters for the first step search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter; if the second charging efficiency parameters meet the flow conditions of the second step search phase, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step search phase based on the multiple second charging efficiency parameters; performs iterative calculations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine a convergence efficiency parameter; and determines the power parameters for the next cycle based on the convergence efficiency parameter and performs tracking and control to maximize the charging pile operating efficiency in the next cycle. In this way, under fixed demand, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the efficiency range limitations of the charging pile, allowing the equipment to continuously operate in the maximum efficiency range, and reducing overall losses.

[0102] Please refer to Figure 5 , Figure 5 This is a flow chart of another method for optimizing the charging efficiency of a charging pile provided in an embodiment of the present application. Figure 5 As shown, the method includes: obtaining multiple power parameters for the current cycle, determining multiple first charging efficiency parameters, performing a first step search, determining multiple second charging efficiency parameters for the first step search phase, and 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 drop threshold. If so, entering a second step search phase; if not, repeating the first step search phase. In the second step search phase, multiple golden section efficiency points and multiple first split point efficiency parameters for the second step search phase are determined, and iterative calculation operations are performed based on the multiple golden section efficiency points and multiple first split point efficiency parameters to determine a first convergence judgment result and determine whether convergence has occurred. If so, the power parameters for the next cycle are determined based on the converged efficiency parameters and tracking and regulating them. If not, the second step search phase is repeated and iterated until convergence has occurred or the number of iterations exceeds a preset iteration threshold, at which point the iterations are terminated and the process ends.

[0103] See Figure 6 , Figure 6 Schematic diagram of the structure of a charging pile charging efficiency optimization device proposed in an embodiment of the present application. The charging pile charging efficiency optimization device 600 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.

[0104] Among them, the acquisition module 610 is used to obtain multiple power parameters of the current cycle, and to determine multiple first charging efficiency parameters based on the multiple power parameters of the current cycle; the first search module 620 is used to determine multiple second charging efficiency parameters of the first step 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 630 is used to determine multiple golden section efficiency points and multiple first segmentation point efficiency parameters of the second step search phase based on the multiple second charging efficiency parameters if the second charging efficiency parameter meets the flow conditions of the second step search phase; the convergence iteration module 640 is used to perform iterative calculation operations based on the multiple golden section efficiency points and multiple first segmentation point efficiency parameters to determine the convergence efficiency parameters; the tracking and control module 650 is used to determine the power parameters of the next cycle based on the convergence efficiency parameters and perform tracking and control to maximize the operating efficiency of the charging pile in the next cycle.

[0105] In a possible embodiment, the multiple power parameters of the current cycle include multiple current input power parameters and multiple current output power parameters. The acquisition module 610, in determining the multiple first charging efficiency parameters based on the multiple power parameters of the current cycle, is specifically configured to: determining a plurality of current charging efficiencies based on the plurality of current input power parameters and the plurality of current output power parameters; Obtaining a power grid power frequency cycle, where the power grid power frequency cycle is the operating frequency of the power grid where the target charging pile is located; Determining an attenuation weight factor based on the power frequency cycle of the power grid; The multiple first charging efficiency parameters are determined based on the attenuation weight factor and the multiple current charging efficiencies, and the multiple first charging efficiency parameters are correspondingly associated with the multiple current input power parameters and / or the multiple current output power parameters.

[0106] In a possible embodiment, the first search module 620, in determining the multiple second charging efficiency parameters of the first long search phase based on the multiple power parameters, the first charging efficiency parameter, the multiple efficiency thresholds, and the first power parameter, is specifically configured to: determining a first step length based on the first power parameter, and determining a first search power interval based on the multiple power parameters; performing a first search operation based on the first step length, the first search power interval, and initialization information, the initialization information including a starting power parameter; The first search operation includes: searching in the first search power interval based on the starting power and the first step length, and determining a sampling power parameter and a sampling efficiency parameter of a current sampling point, wherein the sampling power parameter of the current sampling point is one of the multiple power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the multiple second charging efficiency parameters.

[0107] In a possible embodiment, the second charging efficiency parameter includes at least one of a sampling efficiency parameter of a next sampling point and a sampling efficiency parameter of the current sampling point. The transition condition of the second step search phase includes: the sampling efficiency parameter of the current sampling point is greater than a preset efficiency threshold, and / or a difference parameter 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 degradation threshold.

[0108] In a possible embodiment, the second search module 630, in determining the multiple golden section efficiency points and the multiple first section point efficiency parameters in the second step search phase based on the multiple second charging efficiency parameters, is specifically configured to: determining a second step size based on the first power parameter, and determining a second search power interval based on the second step size and the sampling power parameter of the current sampling point; Obtaining 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 a plurality of first-type golden sampling segmentation points based on the anti-disturbance factor, the second search power interval, and the golden segmentation parameter; The plurality of first segmentation point efficiency parameters corresponding to the plurality of first-type golden sampling segmentation points are obtained.

[0109] In a possible embodiment, the convergence iteration module 640, in performing 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 parameter, is specifically configured to: determining a third search power interval and a plurality of second split point efficiency parameters based on the plurality of first split point efficiency parameters; determining a fourth search power interval and a plurality of third segmentation point efficiency parameters based on the third search power interval and the golden section parameter; determining a first convergence judgment result based on the second segmentation point efficiency parameter, the third segmentation point efficiency parameter, and a preset efficiency difference threshold; Determining a second convergence judgment result based on the fourth search power interval and a preset interval threshold; A convergence efficiency parameter is determined based on the first convergence judgment result and the second convergence judgment result.

[0110] In a possible embodiment, the tracking and regulating module 650 is specifically configured to: Determining a 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 of the next cycle; Obtaining a third charging efficiency parameter and a second power parameter; determining an efficiency change rate parameter based on a 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; Tracking and regulation are performed based on the dynamic regulation decision and the power parameters of the next cycle.

[0111] It is worth noting that the specific functional implementation of the charging efficiency optimization device 600 of the charging pile can be found in the above Figure 2 The description of the charging efficiency optimization method for a charging pile shown in FIG. For example, the acquisition module 610 is used to implement the relevant content of execution S210, the first search module 620 is used to implement the relevant content of execution S220, the second search module 630 is used to implement the relevant content of execution S230, the convergence and iteration module 640 is used to implement the relevant content of execution S240, and the tracking and control module 650 is used to implement the relevant content of execution S250. The various units or modules in the charging efficiency optimization device 600 for charging piles can be individually or completely combined 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 to achieve the same operation without affecting the technical effects of the embodiments of the present invention. The above-mentioned units or modules are divided based on logical functions. In actual applications, the functions of one unit (or module) are implemented by multiple units (or modules), or the functions of multiple units (or modules) are implemented by one unit (or module).

[0112] As can be seen, the charging pile charging efficiency optimization device 600 described in the embodiment of the present 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 of the current cycle; determines multiple second charging efficiency parameters for the first step search phase based on the multiple power parameters, the first charging efficiency parameter, multiple efficiency thresholds, and the first power parameter; if the second charging efficiency parameters meet the flow conditions of the second step search phase, determines multiple golden section efficiency points and multiple first segmentation point efficiency parameters for the second step search phase 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 a convergence efficiency parameter; and determines the power parameters for the next cycle based on the convergence efficiency parameter and performs tracking and regulation to maximize the charging pile operating efficiency in the next cycle. In this way, under fixed demand, real-time fitting of the voltage-power optimal solution can be achieved, breaking through the efficiency range limit of the charging pile, allowing the equipment to continuously operate in the maximum efficiency range, and reducing overall losses.

[0113] In the case of integrated units, see Figure 7 , Figure 7 Schematic diagram of another charging efficiency optimization device for a charging pile provided in an embodiment of the present application. Figure 7 As shown, the charging efficiency optimization device 600 for the charging pile includes: a processing module 602 and a communication module 601. The processing module 602 is used to control and manage the actions of the charging efficiency optimization device 600 for the charging pile, 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, the tracking and control module 650, and / or other processes for executing the technology described in this article. The communication module 601 is used for the interaction between the charging efficiency optimization device 600 for the charging pile and other devices. Figure 7 As shown, the charging efficiency optimization device 600 for the charging pile may further include a storage module 603 , and the storage module 603 is used to store program codes and data of the charging efficiency optimization device 600 for the charging pile.

[0114] The processing module 602 may 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 may implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor may 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, and the like. The communication module 601 may be a transceiver, an RF circuit, or a communication interface, and the like. The storage module 603 may be a memory.

[0115] Among them, all relevant contents of each scenario involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here. The charging efficiency optimization device 600 of the above charging pile can execute the above Figure 2 The charging efficiency optimization method of the charging pile shown.

[0116] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device proposed in an embodiment of the present application. 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 . The one or more programs 821 are stored in the memory 820 and are configured to be executed by the processor 810 .

[0117] The processor 810, the memory 820, and the communication interface 830 are interconnected and perform communication between them. The memory 820 may be a volatile memory such as a dynamic random access memory (DRAM) or a non-volatile memory such as a mechanical hard disk. The memory 820 is used to store a set of executable program codes, and the processor 810 is used to call 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-mentioned embodiments of the method for optimizing the charging efficiency of charging piles.

[0118] Among them, the electronic device 800 may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, driving recorders, vehicle-mounted electronic devices, servers, laptops, mobile Internet electronic devices (MID, Mobile Internet Devices) or wearable electronic devices (such as smart watches, Bluetooth headsets), etc. The above are only examples and not exhaustive, including but not limited to the above electronic devices.

[0119] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0120] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is 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 comprise an electronic device.

[0121] It should be noted that for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0122] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0123] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0124] The units described above as separate components may or may not be physically separate, and 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 these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0125] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0126] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the existing technology, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory and includes a number of instructions for enabling a computer electronic device (which can be a personal computer, electronic device, or network electronic device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, magnetic disk or optical disk, etc. Various media that can store program code.

[0127] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by a program instructing related hardware. The program can be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0128] The above is a detailed introduction to the embodiments of the present application. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present 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, wherein the charging efficiency optimization system further includes a plurality of target charging piles, and the method includes: Acquiring a plurality of power parameters of a current cycle, and determining a plurality of first charging efficiency parameters based on the plurality of power parameters of the current cycle; Determining a plurality of second charging efficiency parameters in a first length search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of efficiency thresholds, and the first power parameter; If the second charging efficiency parameter meets the flow condition of the second step search phase, determining a plurality of golden section efficiency points and a plurality of first section point efficiency parameters of the second step search phase based on the plurality of second charging efficiency parameters; Performing an iterative calculation operation based on the multiple golden section efficiency points and the multiple first section point efficiency parameters to determine a convergence efficiency parameter; The power parameters of the next cycle are determined based on the convergence efficiency parameters and tracked and regulated 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, and determining multiple first charging efficiency parameters based on the multiple power parameters of the current cycle includes: determining a plurality of current charging efficiencies based on the plurality of current input power parameters and the plurality of current output power parameters; Obtaining a power grid power frequency cycle, where the power grid power frequency cycle is the operating frequency of the power grid where the target charging pile is located; Determining an attenuation weight factor based on the power frequency cycle of the power grid; The multiple first charging efficiency parameters are determined based on the attenuation weight factor and the multiple current charging efficiencies, and the multiple first charging efficiency parameters are correspondingly associated with the multiple current input power parameters and / or the multiple current output power parameters.

3. The method according to claim 1, characterized in that The determining of a plurality of second charging efficiency parameters in the first step length search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of 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 interval based on the multiple power parameters; performing a first search operation based on the first step length, the first search power interval, and initialization information, the initialization information including a starting power parameter; The first search operation includes: searching in the first search power interval based on the starting power and the first step length, and determining a sampling power parameter and a sampling efficiency parameter of a current sampling point, wherein the sampling power parameter of the current sampling point is one of the multiple power parameters, and the sampling efficiency parameter of the current sampling point is at least one of the multiple second charging efficiency parameters.

4. The method according to claim 3, characterized in that The second charging efficiency parameter includes at least one of a sampling efficiency parameter of a next sampling point and a 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 a difference parameter 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 degradation threshold.

5. The method according to claim 4, characterized in that The determining of the plurality of golden section efficiency points and the plurality of first section point efficiency parameters in the second step search phase based on the plurality of second charging efficiency parameters comprises: determining a second step size based on the first power parameter, and determining a second search power interval based on the second step size and the sampling power parameter of the current sampling point; Obtaining 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 a plurality of first-type golden sampling segmentation points based on the anti-disturbance factor, the second search power interval, and the golden segmentation parameter; The plurality of first segmentation point efficiency parameters corresponding to the plurality of first-type golden sampling segmentation points are obtained.

6. The method according to claim 5, characterized in that 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 parameter includes: determining a third search power interval and a plurality of second split point efficiency parameters based on the plurality of first split point efficiency parameters; determining a fourth search power interval and a plurality of third segmentation point efficiency parameters based on the third search power interval and the golden section parameter; determining a first convergence judgment result based on the second segmentation point efficiency parameter, the third segmentation point efficiency parameter, and a preset efficiency difference threshold; Determining a second convergence judgment result based on the fourth search power interval and a preset interval threshold; A 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 determining the power parameter of the next cycle based on the convergence efficiency parameter and performing tracking and regulation includes: Determining a 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 of the next cycle; Obtaining a third charging efficiency parameter and a second power parameter; determining an efficiency change rate parameter based on a 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; Tracking and regulation are performed based on the dynamic regulation 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, wherein the charging efficiency optimization system further comprises a plurality of target charging piles, and the device comprises: an acquisition module, configured to acquire a plurality of power parameters of a current cycle, and determine a plurality of first charging efficiency parameters based on the plurality of power parameters of the current cycle; a first search module, configured to determine a plurality of second charging efficiency parameters in a first long search phase based on the plurality of power parameters, the first charging efficiency parameter, a plurality of efficiency thresholds, and the first power parameter; a second search module, configured to determine, if the second charging efficiency parameter meets the flow condition of the second step search phase, a plurality of golden section efficiency points and a plurality of first section point efficiency parameters of the second step search phase based on the plurality of second charging efficiency parameters; a convergence iteration module, configured to perform an iterative calculation operation based on the plurality of golden section efficiency points and the plurality of first section point efficiency parameters to determine a convergence efficiency parameter; The tracking and control module is used to determine the power parameters of the next cycle based on the convergence efficiency parameters and perform tracking and control to maximize the operating efficiency of the charging pile in the next cycle.

9. A computer-readable storage medium, characterized in that A charging efficiency optimization program for a charging pile is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method according to any one of claims 1 to 7.

10. An electronic device, characterized in that: comprising a processor, a memory, a communication interface, and one or more programs, the one or more programs being 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 according to any one of claims 1 to 7.

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