Charging pile power distribution device and method, electronic equipment and medium

Abstract

The invention provides a charging pile power distribution device and method, electronic equipment and a medium. The device comprises a vehicle information acquisition module, a trickle charging state prediction module and a power pre-scheduling and smooth transfer module. The vehicle information acquisition module establishes communication with a battery management system of a charging vehicle, and collects and transmits battery state data and charging demand parameters in a vehicle charging process; the trickle charging state prediction module receives the battery state data and the charging demand parameters, predicts whether the vehicle enters a charging end period or not through multi-stage judgment logic, and outputs an end period identification signal when it is judged that the vehicle enters the charging end period; and the power pre-scheduling and smooth transfer module is used for receiving the last-stage identification signal and the vehicle charging demand parameters, adjusting the charging resource allocation quantity of the corresponding vehicle based on the last-stage identification signal, and reallocating the released charging resources according to the charging demand parameters of the other vehicles so as to realize dynamic optimization scheduling of the total charging resources of the charging piles.

Description

Technical Field

[0001] This invention relates to the field of charging pile technology, specifically to a charging pile power distribution device, method, electronic device, and medium. Background Technology

[0002] As the global energy structure shifts towards cleaner and lower-carbon energy, electric vehicles, as the core carrier of new energy applications, are experiencing a rapid and continuous increase in market penetration. Against this backdrop, high-power charging stations capable of meeting rapid energy replenishment needs have become a crucial social infrastructure supporting the large-scale application of electric vehicles. They are widely deployed in public stations, highway service areas, residential communities, and other scenarios, directly impacting user travel experience and the development pace of the electric vehicle industry.

[0003] To address the practical constraints of limited grid access capacity and maximize the service efficiency and operational benefits of charging stations, high-power charging piles integrating multiple charging modules and dynamically allocating power are gradually becoming the mainstream equipment form. Currently, the power allocation methods for these charging piles are mostly based on dynamic power sharing technology. Typical strategies include first-come, first-served sequential allocation mechanisms and differentiated mechanisms that prioritize the charging needs of pure electric vehicles. Their allocation logic usually relies on the number of connected vehicles, the real-time state of charge (SOC) of the vehicles, or simple parameters preset by the user (such as the expected target SOC and the scheduled charging start time) to achieve a basic allocation of total power among multiple vehicles.

[0004] However, existing power allocation schemes still have significant technical shortcomings in practical applications, making it difficult to adapt to the charging characteristics of electric vehicle lithium batteries and the needs of efficient station operation. Specifically, these shortcomings are reflected in the following three aspects: Ignoring the power mismatch caused by the nonlinear charging characteristics of batteries: The charging process of lithium batteries in electric vehicles exhibits significant nonlinear characteristics and is not uniform charging. When the real-time SOC value of the battery approaches 80% or higher, in order to avoid overcharging, suppress the risk of thermal runaway, and extend the battery cycle life, the Battery Management System (BMS) will actively trigger a protection mechanism, significantly reducing the maximum allowable charging current and putting the battery into the trickle charging stage. In this stage, the actual charging power of more than 90% of models will drop to below 40kW, far below the rated output power of a single module or multi-module combination of the charging stack.

[0005] Power release lag: Traditional power allocation systems employ a reactive, after-the-fact detection and adjustment model, lacking proactive predictive capabilities. Taking a multi-vehicle charging scenario at a single charging station as an example, when vehicle A's BMS has initiated trickle charging and its actual charging power demand decreases, the system needs to go through multiple stages of delay, including data acquisition, signal transmission, and power detection, before it can identify the change in vehicle A's power demand and initiate the charging module release process to allocate power to other vehicles waiting to be charged. This lag typically lasts from tens of seconds to several minutes, during which charging modules that could have served other vehicles are ineffectively occupied by vehicle A, resulting in a time-consuming waste of power resources.

[0006] Insufficient overall power utilization: During peak charging periods, if multiple electric vehicles simultaneously enter the final stage of charging (i.e., trigger trickle charging at the same time), the aforementioned lag defects will be further amplified. A large number of charging modules will be idle due to system response delays, and the total power resources of the charging station cannot be fully utilized. This not only prolongs the waiting time for vehicles that subsequently connect, but also reduces the number of vehicles served per unit time at the station (i.e., turnover rate), directly affecting the station's operating revenue and user charging efficiency.

[0007] In summary, existing charging pile power allocation schemes based on passive response and ignoring the characteristics of the battery charging end phase can no longer meet the core requirements of efficient operation of high-power charging piles and rapid energy replenishment for users. Therefore, there is an urgent need to provide a technical solution that can accurately predict the characteristics of electric vehicles at the end of their charging phase and actively optimize power allocation to solve the problems of power waste and low efficiency caused by traditional allocation methods, and promote the development of charging pile systems towards a more intelligent and efficient direction. Summary of the Invention

[0008] In view of this, the present disclosure provides a charging pile power distribution device, method, electronic device and medium, which at least partially solves the problems existing in the prior art.

[0009] In a first aspect, embodiments of this disclosure provide a charging pile power distribution device, which includes: Vehicle information acquisition module, trickle charging status prediction module, and power pre-scheduling and smooth transfer module; The vehicle information acquisition module is used to establish real-time deep communication with the battery management system of the charging vehicle, and to collect and transmit battery status data and charging demand parameters during the vehicle charging process. The trickle charging state prediction module is used to receive the battery state data and the charging demand parameters, predict whether the vehicle has entered the end of the charging period through a preset multi-stage judgment logic, and output the end-of-charging period identification signal when it is determined that the vehicle has entered the end of the charging period. The power pre-scheduling and smooth transfer module is used to receive the end-of-term indicator signal and the charging demand parameters of the vehicle, adjust the charging resource allocation of the corresponding vehicle based on the end-of-term indicator signal, and redistribute the released charging resources according to the charging demand parameters of the remaining vehicles, so as to realize the dynamic optimization scheduling of the total charging resources of the charging pile.

[0010] According to a specific implementation of this disclosure, the battery status data and charging demand parameters include at least one of the following: real-time vehicle SOC value, maximum allowable charging current, maximum allowable charging voltage, highest or lowest individual cell temperature of the battery, and real-time required charging voltage and current data.

[0011] According to a specific implementation of an embodiment of this disclosure, the multi-stage judgment logic includes: a first stage triggering an end-of-charge monitoring mode based on the real-time SOC value in the battery state data; and a second stage calculating the change slope based on the current data in the charging demand parameters, and determining whether to enter the end of the charging period by combining the slope threshold and the continuous judgment condition.

[0012] According to a specific implementation of an embodiment of this disclosure, the trickle charging state prediction module is used to preset a real-time SOC threshold to trigger a terminal monitoring mode. The real-time SOC threshold ranges from 75% to 85%. When the real-time SOC value of the vehicle is detected to reach the real-time SOC threshold, the terminal monitoring mode is automatically started and current data is recorded.

[0013] According to a specific implementation of this disclosure, the trickle charging state prediction module uses a sliding window algorithm to process the recorded current data and calculates the slope of the change in the current data within the window. The data source of the sliding window is the current data in the charging demand parameters collected after the end-of-life monitoring mode is triggered.

[0014] According to a specific implementation of an embodiment of this disclosure, the trickle charging state prediction module is used to preset a slope determination threshold and a number of continuous determinations. When the current change slope calculated based on the preset number of continuous determinations is less than the slope determination threshold, the vehicle is determined to have entered the final stage of charging and an end-of-stage indicator signal is output.

[0015] According to a specific implementation of an embodiment of this disclosure, the power pre-scheduling and smooth transfer module is used to gradually reduce the charging output current of the corresponding vehicle by a preset current decrease rate until it is adjusted to the output current corresponding to the minimum allocation unit. The power pre-scheduling and smooth transfer module is also used to reallocate the released charging resources according to the charging demand parameters of the other vehicles, and to allocate the released charging resources to the other vehicles in order of increasing real-time SOC value, with the vehicle with the smaller real-time SOC value being allocated more charging resources.

[0016] Secondly, embodiments of this disclosure provide a charging pile power allocation method, applicable to the charging pile power allocation device according to any one of the first aspects, the method comprising: By establishing real-time deep communication with the battery management system of the charging vehicle, battery status data and charging demand parameters during the vehicle charging process are collected and transmitted. Based on the battery status data and the charging demand parameters, a preset multi-stage judgment logic is used to predict whether the vehicle has entered the end of the charging period, and an end-of-charging indicator signal is output when it is determined that the vehicle has entered the end of the charging period. The charging resource allocation for the corresponding vehicle is adjusted based on the end-of-life identification signal, and the released charging resources are redistributed according to the charging demand parameters of the remaining vehicles, so as to achieve dynamic optimization scheduling of the total charging resources of the charging pile.

[0017] Thirdly, embodiments of this disclosure provide an electronic device, the electronic device comprising: At least one processor; and, The memory is communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the above-described charging pile power distribution method.

[0018] Fourthly, embodiments of this disclosure provide a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the aforementioned charging pile power allocation method.

[0019] In summary, compared with the prior art, this embodiment has the following advantages: 1. Significantly improve power utilization: By predicting and scheduling the end of the charging period in advance, the idle power resources caused by the lag in the response of traditional power allocation methods are avoided, and the utilization efficiency of the charging pile power module is greatly improved.

[0020] 2. Shorten the average charging time at the depot and improve vehicle turnover: The efficient use of power resources shortens the effective charging time for each vehicle, especially for vehicles with lower real-time SOC values ​​that are connected later, which can obtain high power support more quickly; and after the vehicle enters the trickle charging stage, most car owners will actively stop charging in advance, thereby significantly improving the vehicle turnover rate at the depot.

[0021] 3. Improved user charging experience: For users with urgent charging needs, the ideal charging power can be obtained faster, reducing unnecessary waiting time and improving user satisfaction with the charging experience.

[0022] In summary, this embodiment achieves the prediction of when an electric vehicle will enter the trickle charging stage by collecting battery data and establishing a multi-dimensional prediction model. Then, it adjusts the power allocation strategy accordingly to overcome the pain point of low efficiency in traditional allocation methods. It can schedule power modules in advance, optimize resource allocation logic, effectively reduce power waste, and significantly improve the energy utilization efficiency of the charging pile, thus solving the efficiency problem of traditional allocation methods. Attached Figure Description

[0023] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0024] The term "comprising" and its variations as used herein signify open inclusion, i.e., "including but not limited to". Unless otherwise stated, the term "or" means "and / or". The term "based on" means "at least partially based on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment". The term "another embodiment" means "at least one additional embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0025] Figure 1 This is a schematic diagram of a charging pile power distribution device provided in an embodiment of this application; Figure 2 A schematic diagram of a trickle charging state prediction module provided in an embodiment of this application; Figure 3 A schematic diagram of the logic flow for determining the end of charging stage provided in an embodiment of this application; Figure 4 A schematic diagram of a power pre-scheduling and smooth transfer module provided in an embodiment of this application; Figure 5 A flowchart of a charging pile power allocation method provided in an embodiment of this application; Figure 6 An exemplary structural diagram of a device capable of implementing the method according to an embodiment of the present invention is shown. Detailed Implementation

[0026] The embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0027] The following specific examples illustrate the implementation of this disclosure. Those skilled in the art can easily understand other advantages and effects of this disclosure from the content disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. This disclosure can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of this disclosure. It should be noted that, in the absence of conflict, the following embodiments and features in the embodiments can be combined with each other. Based on the embodiments in this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0028] It should be noted that various aspects of embodiments within the scope of the appended claims are described below. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this disclosure, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0029] It should also be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of this disclosure. The drawings only show the components related to this disclosure and are not drawn according to the number, shape and size of the components in actual implementation. In actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0030] Furthermore, specific details are provided in the following description to facilitate a thorough understanding of the examples. However, those skilled in the art will understand that the described aspects can be practiced without these specific details.

[0031] Please see Figure 1 This application provides an embodiment of a charging pile power distribution device, such as... Figure 1 The charging pile power distribution device shown includes: a vehicle information acquisition module, a trickle charging status prediction module, and a power pre-scheduling and smooth transfer module.

[0032] First, let's introduce the vehicle information acquisition module.

[0033] The vehicle information acquisition module is used to establish real-time deep communication with the battery management system of the charging vehicle, and to collect and transmit battery status data and charging demand parameters during the vehicle charging process.

[0034] Optional, battery status data and charging demand parameters include at least one of the following: real-time vehicle SOC value, maximum allowable charging current, maximum allowable charging voltage, highest or lowest cell temperature of the battery, and real-time required charging voltage and current data.

[0035] Specifically, the vehicle information acquisition module is responsible for the basic management of the entire charging process, including connecting with the vehicle to start or stop charging (charging process interaction), controlling the hardware equipment of the charging pile (such as power modules, contactors, etc., i.e., pile control), and executing safety protection rules such as overvoltage, overtemperature, and overcurrent to ensure the basic safety and normal operation of the charging process. In addition, the vehicle information acquisition module establishes a dedicated communication channel with the BMS of the charging vehicle directly through the integrated battery management system transceiver, enabling real-time interaction between the two during the charging process.

[0036] The vehicle information acquisition module communicates with the BMS and collects key data. The collected data can be divided into two categories. The first category is battery status data, which reflects the current basic state of the battery, including the vehicle's real-time SOC value and the highest or lowest cell temperature of the battery. The second category is charging demand and limitation data, including the maximum allowable charging current, the maximum allowable charging voltage, and the real-time required charging voltage and current.

[0037] The following describes the trickle charging state prediction module. This module receives battery state data and charging demand parameters, uses preset multi-stage judgment logic to predict whether the vehicle has entered the final stage of charging, and outputs a final stage indicator signal when it determines that the vehicle has entered the final stage of charging.

[0038] The pain point of traditional charging pile power allocation lies in its lag. That is, the system only detects the decrease in power demand and releases resources after the vehicle's BMS actively reduces the charging current and enters the trickle charging phase. During this process, a large number of power modules are idle. However, the trickle charging status prediction module predicts in advance, shifting the power allocation decision point from when the vehicle has already entered the trickle charging phase to when it is about to enter the trickle charging phase. This allows the system sufficient time to schedule power in advance and avoids resource waste.

[0039] Optionally, the multi-stage judgment logic includes: in the first stage, the end-of-charge monitoring mode is triggered based on the real-time SOC value in the battery state data; in the second stage, the change slope is calculated based on the current data in the charging demand parameters, and the slope threshold and continuous judgment conditions are combined to determine whether the charging end-of-charge period has been entered.

[0040] Optionally, a real-time SOC threshold is preset to trigger the end-of-life monitoring mode. The real-time SOC threshold ranges from 75% to 85%. When the real-time SOC value of the vehicle is detected to reach the real-time SOC threshold, the end-of-life monitoring mode is automatically started and current data is recorded.

[0041] Specifically, the trickle charging state prediction module does not rely on a single parameter for judgment, but rather employs a combined model of static threshold triggering and dynamic slope analysis, ensuring both accuracy and avoiding misjudgments. The threshold refers to the preset real-time SOC threshold, which is a preliminary screening condition for determining whether a vehicle is nearing the end of its charging phase. Since most electric vehicles will gradually limit the charging current to protect the battery after the real-time SOC value reaches 80%, setting 75%-85% as the trigger point can filter out vehicles that may be about to enter the trickle charging phase, avoiding ineffective monitoring of vehicles with low real-time SOC values ​​and reducing system computing power consumption.

[0042] The slope refers to the rate of decrease in demand current, which is a core dynamic indicator for determining whether a vehicle is truly approaching the trickle charging stage. Relying solely on the real-time SOC threshold cannot distinguish between situations where the real-time SOC value reaches the threshold but still maintains high charging current (e.g., some new batteries) and situations where the current begins to decrease rapidly after reaching the real-time SOC threshold (e.g., aging batteries). The current decrease slope directly reflects the BMS's (Battery Management System's) trend in limiting the charging current; the larger the slope (the faster the current decreases), the closer the vehicle is to the trickle charging stage, thus enabling more accurate predictions.

[0043] Optionally, the trickle charging state prediction module uses a sliding window algorithm to process the recorded current data and calculate the slope of the change in current data within the window. The data source of the sliding window is the current data in the charging demand parameters collected after the end-of-life monitoring mode is triggered.

[0044] Specifically, see Figure 2 , Figure 2 This is a schematic diagram of a trickle charging state prediction module provided in an embodiment of this application. Figure 2 As shown, the input to the trickle charging state prediction module is the real-time SOC data of the vehicle received in real time through the vehicle information acquisition module. When the real-time SOC value reaches the preset real-time SOC threshold, the end-of-life monitoring mode is immediately triggered.

[0045] Next, the trickle charging state prediction module confirms whether the vehicle has entered the end of its charging phase and outputs the prediction result. In the end-of-phase monitoring mode, the trickle charging state prediction module will continuously collect the vehicle's real-time demand current data and calculate the current drop slope (i.e., the current drop rate per unit time) using the sliding window method.

[0046] Optionally, a trickle charging state prediction module is used to preset a slope determination threshold and a number of continuous determinations. When the current change slope calculated based on the preset number of continuous determinations is less than the slope determination threshold, the vehicle is determined to have entered the end of the charging period and an end-of-charging indicator signal is output.

[0047] For example, if the sliding window is set to the 10 most recent current sampling points (with an interval of 1 second), and the current at these 10 points gradually decreases from 100A to 85A, then the average decreasing slope can be calculated to be 1.5A / second.

[0048] The formula for calculating the linear regression slope of the current within the window is as follows: in, For timestamps, This is the current value. and This represents the mean value within the window.

[0049] Then, the trickle charging state prediction module determines whether it has entered the end of the charging period by the slope change trend. If the current drop slope is continuously greater than the preset threshold (e.g., 1.5A / second, which can be adjusted according to the battery type) and the trend is maintained stably for a certain number of times (e.g., 3 consecutive window updates to avoid misjudgment of current fluctuations caused by instantaneous interference), it is determined that the vehicle is about to enter the trickle charging stage and outputs an end-of-charge warning signal (e.g., end_of_charge_flag).

[0050] The logic flow for determining the end of charging is as follows: Figure 3 As shown, Figure 3 This is a schematic diagram of the logic flow for determining the end of charging as provided in an embodiment of this application.

[0051] Furthermore, the trickle charging status prediction module outputs the judgment result to the monitoring unit controller. Based on the judgment result, the monitoring unit controller schedules the power module in advance (such as releasing idle power or allocating it to other vehicles) to achieve efficient utilization of the charging pile power.

[0052] The following section describes the power pre-scheduling and smooth transfer module. See details below. Figure 4 , Figure 4 This is a schematic diagram of a power pre-scheduling and smooth transfer module provided in an embodiment of this application. Figure 4As shown, when a vehicle enters the final stage of charging (such as the trickle charging phase), its demand for high power decreases. At this time, idle power modules of the vehicle are released, freeing up available power resources. The power pre-scheduling and smooth transfer module pre-allocates the released power modules to other vehicles with lower real-time SOC values ​​that urgently need high-power charging, achieving efficient reuse of charging pile power resources. During the power module reallocation process, the power pre-scheduling and smooth transfer module also ensures a smooth and abrupt switching of charging power, avoiding any impact on the vehicle battery or charging experience due to power fluctuations, and ensuring the stability of the charging process.

[0053] In other words, the power pre-scheduling and smooth transfer module receives the end-of-term indicator signal and the vehicle charging demand parameters, adjusts the charging resource allocation for the corresponding vehicle based on the end-of-term indicator signal, and redistributes the released charging resources according to the charging demand parameters of the remaining vehicles, so as to achieve dynamic optimization scheduling of the total charging resources of the charging pile.

[0054] Optionally, a power pre-scheduling and smooth transfer module is used to gradually reduce the charging output current of the corresponding vehicle by a preset current decrease rate until it is adjusted to the output current corresponding to the minimum allocation unit. When reallocating released charging resources based on the charging demand parameters of other vehicles, the real-time SOC value in the charging demand parameters is used as the core allocation basis. The released charging resources are allocated to other vehicles in ascending order of real-time SOC value, and the vehicles with smaller real-time SOC values ​​are allocated more charging resources.

[0055] Specifically, when the trickle charging status prediction module outputs the end-of-term warning signal, the monitoring unit locks the number of pre-allocated modules to the minimum unit. For example, the monitoring unit directly refreshes the number N of pre-allocated charging modules for the vehicle to 1. Here, 1 is the minimum power allocation unit of the charging pile (i.e., a single charging module, which cannot be further divided), ensuring that the vehicle's trickle charging needs are maintained only with the minimum module (no need for high power).

[0056] Furthermore, the charging current decreases at a preset rate, simultaneously shutting down redundant charging modules. To prevent sudden current changes from damaging the battery, the monitoring unit issues current reduction commands to the charging modules currently supplying power to the vehicle, for example, requiring the current to gradually decrease at a rate of 10A / second. At the same time, it can disconnect the contactors of redundant modules at a rate of one module every 5 seconds (taking a 40kW charging module as an example, each module corresponds to a certain current output, and each time a module is shut down, a portion of the current is reduced), until finally only one minimum module is left to continuously output, completing a smooth transition from high-power charging to trickle charging.

[0057] Then, the power pre-scheduling and smooth transfer module pre-allocates the released remaining modules to other vehicles. After releasing the excess power modules in the first step, the monitoring unit immediately starts the module reallocation process. The core principle is to prioritize meeting the high power needs of vehicles with low real-time SOC values, thereby maximizing overall charging efficiency. The specific process is as follows: First, the real-time SOC values ​​of the vehicles are sorted in reverse order to generate an allocation queue. The monitoring unit collects the real-time SOC data of all other vehicles currently charging (excluding the target vehicle that has entered the final stage) and generates a module allocation queue in ascending order of real-time SOC values ​​(i.e., reverse sorting, with smaller real-time SOC values ​​appearing earlier).

[0058] Then, the remaining modules are allocated according to the queue order, prioritizing vehicles with low real-time SOC values: for example, if two 40kW modules are released in the first step, one module is allocated to the vehicle with the lowest real-time SOC value in the queue, and one module is allocated to the vehicle with the second lowest real-time SOC value; if four modules are released, two modules can be allocated to the vehicle with the lowest real-time SOC value, one module to the vehicle with the second lowest real-time SOC value, and one module to the vehicle with the lowest real-time SOC value. This ensures that vehicles with low real-time SOC values ​​can obtain more high-power modules, quickly increase their charge to the end of the charging period close to the real-time SOC threshold, shorten the overall charging time, and improve the vehicle turnover rate of the depot.

[0059] Secondly, this application also provides a charging pile power allocation method, see details below. Figure 5 This can be executed by an electronic device, which can act as a host computer, specifically by one or more processors within the electronic device, to achieve the following steps: S501: By establishing real-time deep communication with the battery management system of the charging vehicle, the battery status data and charging demand parameters during the vehicle charging process are collected and transmitted.

[0060] Specifically, battery status data and charging demand parameters include at least one of the following: real-time vehicle SOC value, maximum allowable charging current, maximum allowable charging voltage, highest or lowest cell temperature of the battery, and real-time required charging voltage and current data.

[0061] S502. Based on the battery status data and the charging demand parameters, predict whether the vehicle has entered the end of the charging period through a preset multi-stage judgment logic, and output the end-of-charging period indicator signal when it is determined that the vehicle has entered the end of the charging period.

[0062] The multi-stage judgment logic includes: the first stage triggers the end-of-charge monitoring mode based on the real-time SOC value in the battery status data; the second stage calculates the change slope based on the current data in the charging demand parameters, and determines whether to enter the end of the charging period by combining the slope threshold and continuous judgment conditions.

[0063] For example, a preset real-time SOC threshold can be used to trigger the end-of-life monitoring mode. The real-time SOC threshold ranges from 75% to 85%. When the vehicle's real-time SOC value is detected to reach the real-time SOC threshold, the end-of-life monitoring mode is automatically activated and current data recording begins.

[0064] Furthermore, a sliding window algorithm is used to process the recorded current data and calculate the slope of the current data change within the window. The data source of the sliding window is the current data in the charging demand parameters collected after the end-of-life monitoring mode is triggered.

[0065] In addition, a preset slope determination threshold and a number of continuous determinations are set. When the current change slope calculated based on the preset number of continuous determinations is less than the slope determination threshold, the vehicle is determined to be in the final stage of charging and an end-of-charge indicator signal is output.

[0066] S503. Adjust the charging resource allocation of the corresponding vehicle based on the end-of-life identification signal, and redistribute the released charging resources according to the charging demand parameters of the remaining vehicles, so as to achieve dynamic optimization scheduling of the total charging resources of the charging pile.

[0067] Specifically, the charging output current of the corresponding vehicle is gradually reduced by a preset current decrease rate until it is adjusted to the output current corresponding to the smallest distribution unit. The system then reallocates the released charging resources based on the charging demand parameters of the remaining vehicles. The real-time SOC value in the charging demand parameters is used as the core allocation basis. The system allocates the released charging resources to the remaining vehicles in ascending order of real-time SOC value, with vehicles having smaller real-time SOC values ​​receiving more charging resources.

[0068] In summary, compared with the prior art, this embodiment has the following advantages: 1. Significantly improve power utilization: By predicting and scheduling the end of the charging period in advance, the idle power resources caused by the lag in the response of traditional power allocation methods are avoided, and the utilization efficiency of the charging pile power module is greatly improved.

[0069] 2. Shorten the average charging time at the depot and improve vehicle turnover: The efficient use of power resources shortens the effective charging time for each vehicle, especially for vehicles with lower real-time SOC values ​​that are connected later, which can obtain high power support more quickly; and after the vehicle enters the trickle charging stage, most car owners will actively stop charging in advance, thereby significantly improving the vehicle turnover rate at the depot.

[0070] 3. Improved user charging experience: For users with urgent charging needs, the ideal charging power can be obtained faster, reducing unnecessary waiting time and improving user satisfaction with the charging experience.

[0071] In summary, this embodiment achieves the prediction of when an electric vehicle will enter the trickle charging stage by collecting battery data and establishing a multi-dimensional prediction model. Then, it adjusts the power allocation strategy accordingly to overcome the pain point of low efficiency in traditional allocation methods. It can schedule power modules in advance, optimize resource allocation logic, effectively reduce power waste, and significantly improve the energy utilization efficiency of the charging pile, thus solving the efficiency problem of traditional allocation methods.

[0072] The third embodiment of the present invention also provides an electronic device, the electronic device comprising: At least one processor; and, The memory is communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which enables the at least one processor to perform the charging pile power distribution method of any of the foregoing embodiments.

[0073] The fourth embodiment of the present invention also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the charging pile power allocation method described in any of the foregoing embodiments.

[0074] The fifth embodiment of the present invention also provides a computer program product, which includes a computing program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions that, when executed by a computer, cause the computer to execute the charging pile power allocation method of any of the foregoing embodiments.

[0075] The sixth embodiment of the present invention also provides a computer program, which includes program instructions. When the program instructions are executed by a computer, the computer executes the charging pile power allocation method of any of the foregoing embodiments.

[0076] Figure 6 The diagram illustrates a method or device 1000 that can implement embodiments of the present invention. In some embodiments, it may include more or fewer devices than illustrated. In some embodiments, it may be implemented using a single or multiple devices. In some embodiments, it may be implemented using cloud-based or distributed devices.

[0077] like Figure 6As shown, device 1000 includes a processor 1001, which can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) 1002 or programs and / or data loaded from storage portion 1008 into random access memory (RAM) 1003. Processor 1001 may be a multi-core processor or may contain multiple processors. In some embodiments, processor 1001 may include a general-purpose main processor and one or more special coprocessors, such as a central processing unit (CPU), graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Random access memory 1003 also stores various programs and data required for the operation of device 1000. Processor 1001, read-only memory 1002, and random access memory 1003 are interconnected via bus 1004. Input / output (I / O) interface 1005 is also connected to bus 1004.

[0078] The processor and memory described above are used together to execute a program stored in the memory. When the program is executed by a computer, it can implement the methods, steps, or functions described in the above embodiments.

[0079] The following components are connected to I / O interface 1005: an input section 1006 including a keyboard, mouse, touchscreen, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to I / O interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 1010 as needed so that computer programs read from it can be installed into storage section 1008 as needed. Figure 6 The diagram only shows a portion of the components and does not imply that the device 1000 only includes... Figure 6 The components shown.

[0080] The systems, devices, modules, or units described in the above embodiments can be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, smartphone, personal computer, laptop computer, in-vehicle human-machine interface device, personal digital assistant, media player, navigation device, game console, tablet computer, wearable device, smart TV, Internet of Things system, smart home, industrial computer, server, or a combination thereof.

[0081] Although not shown, in this embodiment of the invention, a computer-readable storage medium is provided, on which a computer program / instruction is stored, which, when executed by a processor, implements the charging pile power allocation method described in Embodiment 2.

[0082] Storage media in embodiments of the present invention include articles that are permanent and non-permanent, removable and non-removable, capable of storing information by any method or technology. Examples of storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0083] Although not shown, embodiments of the present invention also provide a computer program product, including: a computer program / instructions that, when executed by a processor, implement the charging pile power allocation method described in Embodiment 1.

[0084] The methods, programs, systems, apparatuses, etc., in embodiments of the present invention can be executed or implemented in one or more networked computers, or practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be performed by remote processing devices connected via a communication network.

[0085] Those skilled in the art will understand that the embodiments described in this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art will realize that the functional modules / units or controllers and related method steps described in the above embodiments can be implemented in software, hardware, or a combination of both.

[0086] Unless explicitly stated otherwise, the actions or steps of the methods and procedures described in the embodiments of the present invention do not necessarily have to be performed in a specific order and can still achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0087] This document describes several embodiments of the present invention; however, for the sake of brevity, the descriptions of the embodiments are not exhaustive, and identical or similar features or parts between the embodiments may be omitted. In this document, "one embodiment," "some embodiments," "example," "specific example," or "some examples" refers to embodiments applicable to at least one, but not all, of the present invention. The above terms do not necessarily refer to the same embodiments or examples. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described herein, as well as the features of the different embodiments or examples.

[0088] The exemplary systems and methods of the present invention have been specifically shown and described with reference to the above embodiments, which are merely examples of the best mode for implementing the systems and methods. Those skilled in the art will understand that various changes can be made to the embodiments of the systems and methods described herein without departing from the spirit and scope of the invention as defined in the appended claims when implementing the systems and / or methods.

Claims

1. A power distribution device for a charging pile, characterized in that, The charging pile power distribution device includes: a vehicle information acquisition module, a trickle charging status prediction module, and a power pre-scheduling and smooth transfer module. The vehicle information acquisition module is used to establish real-time deep communication with the battery management system of the charging vehicle, and to collect and transmit battery status data and charging demand parameters during the vehicle charging process. The trickle charging state prediction module is used to receive the battery state data and the charging demand parameters, predict whether the vehicle has entered the end of the charging period through a preset multi-stage judgment logic, and output the end-of-charging period identification signal when it is determined that the vehicle has entered the end of the charging period. The power pre-scheduling and smooth transfer module is used to receive the end-of-term indicator signal and the charging demand parameters of the vehicle, adjust the charging resource allocation of the corresponding vehicle based on the end-of-term indicator signal, and redistribute the released charging resources according to the charging demand parameters of the remaining vehicles, so as to realize the dynamic optimization scheduling of the total charging resources of the charging pile.

2. The charging pile power distribution device according to claim 1, characterized in that, The battery status data and charging demand parameters include at least one of the following: the vehicle's real-time state of charge (SOC) value, maximum allowable charging current, maximum allowable charging voltage, the highest or lowest cell temperature of the battery, and real-time required charging voltage and current data.

3. The charging pile power distribution device according to claim 1, characterized in that, The multi-stage judgment logic includes: in the first stage, the end-of-life monitoring mode is triggered based on the real-time SOC value in the battery status data; in the second stage, the slope of change is calculated based on the current data in the charging demand parameters, and the slope threshold and continuous judgment conditions are combined to determine whether the charging end-of-life phase has been entered.

4. The charging pile power distribution device according to claim 1, characterized in that, The trickle charging state prediction module is used to preset a real-time SOC threshold to trigger the end-of-life monitoring mode. The real-time SOC threshold ranges from 75% to 85%. When the real-time SOC value of the vehicle is detected to reach the real-time SOC threshold, the end-of-life monitoring mode is automatically started and current data is recorded.

5. The charging pile power distribution device according to claim 1, characterized in that, The trickle charging state prediction module uses a sliding window algorithm to process the recorded current data and calculate the slope of the change in the current data within the window. The data source of the sliding window is the current data in the charging demand parameters collected after the end-of-life monitoring mode is triggered.

6. The charging pile power distribution device according to claim 1, characterized in that, The trickle charging state prediction module is used to preset the slope determination threshold and the number of continuous determinations. When the current change slope calculated based on the preset number of continuous determinations is less than the slope determination threshold, the vehicle is determined to have entered the end of the charging period and an end-of-charging indicator signal is output.

7. The charging pile power distribution device according to claim 1, characterized in that, The power pre-scheduling and smooth transfer module is used to gradually reduce the charging output current of the corresponding vehicle by a preset current decrease rate until it is adjusted to the output current corresponding to the minimum allocation unit. The power pre-scheduling and smooth transfer module is also used to reallocate the released charging resources according to the charging demand parameters of the other vehicles, and to allocate the released charging resources to the other vehicles in ascending order of real-time SOC value, with the vehicle with the smaller real-time SOC value being allocated more charging resources.

8. A power allocation method for charging piles, characterized in that, The method, applicable to the power distribution device for a charging pile according to any one of claims 1-7, comprises: By establishing real-time deep communication with the battery management system of the charging vehicle, battery status data and charging demand parameters during the vehicle charging process are collected and transmitted. Based on the battery status data and the charging demand parameters, a preset multi-stage judgment logic is used to predict whether the vehicle has entered the end of the charging period, and an end-of-charging indicator signal is output when it is determined that the vehicle has entered the end of the charging period. The charging resource allocation for the corresponding vehicle is adjusted based on the end-of-life identification signal, and the released charging resources are redistributed according to the charging demand parameters of the remaining vehicles, so as to achieve dynamic optimization scheduling of the total charging resources of the charging pile.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to said at least one processor; The memory stores instructions that can be executed by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the charging pile power allocation method according to claim 8.

10. A non-transitory computer-readable storage medium, characterized in that, The non-transitory computer-readable storage medium stores computer instructions, which are used to cause the computer to execute the charging pile power allocation method of claim 8.

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