Intelligent charging method and system for charging and swapping cabinet

Abstract

The present application provides an intelligent charging method and system for a charging and swapping cabinet, which extracts the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determines the power load of the charging and swapping cabinet by the opening frequency and the charging performance in each battery state, and determines the charging priority of each battery pack according to the power load and the battery swapping frequency of each battery pack; extracts the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between each battery pack in the charging and swapping cabinet, determines the load balance of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power; and dynamically adjusts the charging power of each battery pack in the charging and swapping cabinet in the subsequent charging process according to the load balance and the charging priority of each battery pack. Based on the above scheme, the power distribution efficiency during the charging process can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent charging technology, and more specifically, to an intelligent charging method and system for a charging and swapping cabinet. Background Art

[0002] As part of the charging infrastructure for electric vehicles, charging and swapping cabinets are mainly used to provide battery charging and replacement services. With the popularity of electric vehicles, the demand for charging and swapping cabinets is growing, and they have become an important facility to solve the problem of electric vehicle endurance. Charging and swapping cabinets also integrate big data and Internet of Things technologies, and can perform remote monitoring, battery health management and other functions, improving the intelligent level of battery management. Charging and swapping cabinets play an important role in alleviating problems such as long charging time and tight charging pile resources, and have become an important supporting facility for future smart transportation and green travel.

[0003] Traditional charging and swapping cabinets usually rely on fixed charging power and simple charging load scheduling strategies. However, with the advancement of battery technology and the diversification of electric vehicle types, traditional methods can no longer meet the growing electricity demand and battery health management requirements. Therefore, intelligent charging technology has emerged, aiming to achieve load balancing and efficiency optimization during the charging process by real-time monitoring of battery status, automatic adjustment of charging strategies and power distribution. Therefore, how to achieve dynamic adjustment of the charging power of the battery pack in the charging and swapping cabinet, thereby improving the power distribution efficiency during the charging process, is a difficult problem faced by the industry. Summary of the invention

[0004] The present application provides an intelligent charging method and system for a charging and swapping cabinet, which can realize dynamic adjustment of the charging power of the battery pack in the charging and swapping cabinet in combination with the battery status and the battery swapping frequency, thereby improving the power allocation efficiency during the charging process.

[0005] In a first aspect, the present application provides an intelligent charging method for a charging and swapping cabinet, comprising:

[0006] During the charging process of the charging and swapping cabinet, the battery status of each battery pack in the charging and swapping cabinet is monitored under multiple charging powers;

[0007] Extract the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determine the power load of the charging and swapping cabinet according to the opening frequency and the charging performance in each battery state, and determine the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack;

[0008] Extracting the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and determining the load balance degree of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power;

[0009] The charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process is dynamically adjusted according to the load balancing degree and the charging priority of each battery pack.

[0010] In some embodiments, extracting the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet specifically includes:

[0011] Obtaining the opening interval of each opening of the charging and swapping cabinet from the historical battery swapping record;

[0012] Determine the opening frequency of the charging and swapping cabinet based on all the opening intervals;

[0013] For each battery pack, obtaining the replacement interval of each battery replacement of the battery pack from the historical battery replacement record;

[0014] The replacement frequency of the battery pack is determined based on all replacement intervals, and then the replacement frequency of each battery pack is determined.

[0015] In some embodiments, determining the power load of the charging and swapping cabinet by the opening frequency and the charging performance in each battery state specifically includes:

[0016] Determine the power storage capacity of the charging and swapping cabinet according to the charging performance of each battery state and the stored power in the charging and swapping cabinet;

[0017] Determining the power demand of the charging and swapping cabinet according to the opening frequency;

[0018] The power load of the charging and swapping cabinet is determined by the power demand and the power storage capacity.

[0019] In some embodiments, determining the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack specifically includes:

[0020] For each battery pack in the charging and swapping cabinet, the initial priority of the battery pack is determined according to the battery swapping frequency of the battery pack;

[0021] Determining a priority correction value of the battery pack according to the power load and the remaining power of the battery pack;

[0022] The charging priority of the battery pack is determined according to the priority correction value and the initial priority, and then the charging priority of each battery pack in the charging and swapping cabinet is obtained.

[0023] In some embodiments, extracting the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery packs in the charging and swapping cabinet specifically includes:

[0024] The mapping relationship between each battery pack in the charging and swapping cabinet is constructed based on the state mapping mechanism of the neural network;

[0025] Determine the mapping characteristics of the load in the charging and swapping cabinet according to all mapping relationships;

[0026] The load characteristics of the charging and swapping cabinet are extracted from all battery states through the mapping characteristics.

[0027] In some embodiments, determining the load balance of charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power specifically includes:

[0028] Build an intelligent dispatching mechanism for power distribution in charging and swapping cabinets based on machine learning algorithms;

[0029] Determine a load scheduling strategy in the intelligent scheduling mechanism according to the load characteristics and the multi-channel charging power;

[0030] The load of the power distribution in the load scheduling strategy is balanced and adjusted through the intelligent scheduling mechanism to obtain the load balance degree of the charging power distribution in the charging and swapping cabinet.

[0031] In some embodiments, dynamically adjusting the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack specifically includes:

[0032] When the load balancing degree is lower than the preset balancing threshold, the charging priority of each battery group is used as the allocation priority level of the corresponding battery group in the charging and swapping cabinet in the subsequent charging process, so as to complete the dynamic adjustment of the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process.

[0033] In a second aspect, the present application provides an intelligent charging system for a charging and swapping cabinet, comprising:

[0034] A monitoring module is used to monitor the battery status of each battery pack in the charging and swapping cabinet under multiple charging powers during the charging process of the charging and swapping cabinet;

[0035] A processing module, used to extract the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determine the power load of the charging and swapping cabinet according to the opening frequency and the charging performance in each battery state, and determine the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack;

[0036] The processing module is also used to extract the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and determine the load balance degree of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power;

[0037] The execution module is used to dynamically adjust the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack.

[0038] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned intelligent charging method of the charging and swapping cabinet.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned intelligent charging method for the charging and swapping cabinet when executing.

[0040] The technical solution provided by the embodiments disclosed in this application has the following beneficial effects:

[0041] The present application provides an intelligent charging method and system for a charging and swapping cabinet. During the charging process of the charging and swapping cabinet, the battery status of each battery group in the charging and swapping cabinet is monitored under multiple charging powers; the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery group are extracted from the historical battery swapping record of the charging and swapping cabinet, the power load of the charging and swapping cabinet is determined by the opening frequency and the charging performance in each battery status, and the charging priority of each battery group in the charging and swapping cabinet is determined according to the power load and the battery swapping frequency of each battery group; based on the mapping relationship between each battery group in the charging and swapping cabinet, the load characteristics of the charging and swapping cabinet are extracted from all battery statuses, and the load balance of the charging power distribution in the charging and swapping cabinet is determined according to the load characteristics and the multiple charging powers; the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process is dynamically adjusted according to the load balance and the charging priority of each battery group.

[0042] It can be seen that in the present application, the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process is adaptively adjusted according to the load balancing degree and the charging priority of each battery group; first, the reasonable determination of the charging priority can ensure the high efficiency of power distribution. For example, batteries with poor performance or low remaining power will be given a higher charging priority to avoid excessive discharge of the battery and maintain the battery in a healthy state. The adaptive adjustment mechanism can flexibly allocate power according to the needs of different batteries, avoid overheating or energy waste due to excessive charging of a single battery group, optimize the overall power distribution, and thus improve the overall efficiency of the charging process; then, the load balancing degree is a measure of the uniformity of power distribution of all battery groups in the charging and swapping cabinet during the charging process. Excessive load balancing means that some battery groups may be in an overcharged state due to receiving too much charging power, affecting the health of the battery; while too low load balancing degree may cause some battery groups to be undercharged, affecting the charging efficiency. The determination of the load balancing degree can help ensure that each battery group receives appropriate power distribution during its charging process. For example, when the battery condition is poor or the remaining power of a battery pack is low, the scheduling mechanism will prioritize allocating more power. When the load is high, the intelligent scheduling mechanism will automatically adjust the power distribution to avoid overload or power waste. The charging and swapping cabinet can adaptively adjust the power distribution according to the real-time changes in load balance, thereby maximizing charging efficiency, reducing power waste, and improving the power distribution efficiency during the charging process. In summary, based on the above solution, the battery condition and the battery swapping frequency can be combined to realize dynamic adjustment of the charging power of the battery pack in the charging and swapping cabinet, thereby improving the power distribution efficiency during the charging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1 is an exemplary flow chart of a smart charging method for a charging and swapping cabinet according to some embodiments of the present application;

[0045] Figure 2 It is a diagram of the charging and swapping cabinet architecture shown in some embodiments of the present application;

[0046] Figure 3 is a schematic diagram of a process for determining load characteristics according to some embodiments of the present application;

[0047] Figure 4 It is a structural schematic diagram of an intelligent charging system of a charging and swapping cabinet according to some embodiments of the present application;

[0048] Figure 5 It is a structural schematic diagram of a computer device for implementing an intelligent charging method for a charging and swapping cabinet according to some embodiments of the present application. DETAILED DESCRIPTION

[0049] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0050] refer to Figure 1 , which is an exemplary flow chart of an intelligent charging method for a charging and swapping cabinet according to some embodiments of the present application. The intelligent charging method for the charging and swapping cabinet mainly includes the following steps:

[0051] In step 101, during the charging process of the charging and swapping cabinet, the battery status of each battery pack in the charging and swapping cabinet is monitored under multiple charging powers.

[0052] It should be noted that, in the present application, the battery status refers to the set of the input power, charging speed and remaining power of the battery pack at various charging power levels; in specific implementation, during the charging process of the charging and swapping cabinet, the central console of the charging and swapping cabinet can be used to monitor in real time the input power, charging speed and remaining power of each battery pack at multiple charging powers for each battery pack in the charging and swapping cabinet. The set of input power, charging speed and remaining power can be used as the battery status of the battery pack at multiple charging powers. The battery status of each battery pack in the charging and swapping cabinet at multiple charging powers can be obtained in the above manner, and the battery swapping operation can be recorded by sensors and controllers installed in the charging and swapping cabinet, including the battery swapping timestamp, battery pack serial number and operation type (for example: replacement or charging).

[0053] In some embodiments, reference Figure 2 As described, the figure is a diagram of the charging and swapping cabinet architecture shown in some embodiments of the present application. The figure depicts a charging and swapping cabinet architecture for monitoring and managing the battery charging process. The core of the charging and swapping cabinet is the cabinet control board. The cabinet control board communicates with the remote charging and swapping operation platform through the 4G network to achieve remote transmission of data and receive control instructions. The cabinet control board is connected to the video surveillance device through the LVDS interface and to the human-machine interface through the USB interface to provide an operation interface and monitoring video.

[0054] At the bottom layer of the charging and swapping cabinet, each battery compartment contains multiple battery packs and corresponding charging modules. The battery compartment is a physical space specially designed for the safe storage and management of battery cells. Each battery compartment is equipped with a charging module, which is connected to the battery cell through a DC interface to achieve safe charging of the battery. At the same time, the battery compartment is connected to the cabinet control board through a 485 communication interface, allowing the system to monitor the battery status in real time and perform necessary control operations to ensure the efficient and safe operation of the entire battery management system, and realize real-time monitoring of the battery status and charging control. The cabinet control board also monitors other states of the cabinet through the IO and AD interfaces, such as the cabinet door status, fan status, fire protection status and temperature, to ensure the safe and stable operation of the charging and swapping cabinet. The figure uses solid lines to represent data flow and dotted lines to represent energy flow, clearly distinguishing the paths of information transmission and energy transmission.

[0055] In step 102, the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack are extracted from the historical battery swapping record of the charging and swapping cabinet, the power load of the charging and swapping cabinet is determined by the opening frequency and the charging performance in each battery state, and the charging priority of each battery pack in the charging and swapping cabinet is determined according to the power load and the battery swapping frequency of each battery pack.

[0056] It should be noted that in this application, the historical battery replacement record refers to the collection of historical battery replacement operations in the charging and replacement cabinet; in specific implementation, the collection of all battery replacement operations within a specified time period (the default is the last 1 month) can be used as the historical battery replacement record.

[0057] In some embodiments, extracting the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet can be achieved by the following steps:

[0058] Obtaining the opening interval of each opening of the charging and swapping cabinet from the historical battery swapping record;

[0059] Determine the opening frequency of the charging and swapping cabinet based on all the opening intervals;

[0060] For each battery pack, obtaining the replacement interval of each battery replacement of the battery pack from the historical battery replacement record;

[0061] The replacement frequency of the battery pack is determined based on all replacement intervals, and then the replacement frequency of each battery pack is determined.

[0062] It should be noted that in the present application, the battery replacement frequency is a statistical quantity of the number of times the battery pack in the battery swap cabinet is replaced within a unit time, and the opening frequency refers to the number of times the battery compartment in the battery swap cabinet is opened within a unit time; in specific implementation, first, for each opening of the charging and swapping cabinet, the difference between the battery replacement timestamp corresponding to the battery replacement operation of the opening in the historical battery replacement record and the battery replacement timestamp corresponding to the battery replacement operation of the last opening is used as the opening interval of the compartment, and the opening interval of each opening of the charging and swapping cabinet can be obtained in the above manner, and the opening interval refers to the time interval between two opening operations of the battery swapping cabinet; secondly, the inverse of the mean of all opening intervals can be used as the opening frequency of the charging and swapping cabinet; then, for each battery pack, the battery pack serial number of the battery pack is obtained, and the replacement interval of each battery replacement of the battery pack serial number is obtained from the historical battery replacement record, and the replacement interval refers to the time interval between two battery replacements of the battery pack; finally, the inverse of the mean of all replacement intervals can be used as the battery replacement frequency of the battery pack, and the battery replacement frequency of each battery pack can be obtained in the above manner.

[0063] In some embodiments, determining the power load of the charging and swapping cabinet by the opening frequency and the charging performance in each battery state can be achieved by the following steps:

[0064] Determine the power storage capacity of the charging and swapping cabinet according to the charging performance of each battery state and the stored power in the charging and swapping cabinet;

[0065] Determining the power demand of the charging and swapping cabinet according to the opening frequency;

[0066] The power load of the charging and swapping cabinet is determined by the power demand and the power storage capacity.

[0067] It should be noted that in this application, the power load refers to the total amount of charging power that the charging and swapping cabinet needs to provide additionally. If the power load is a positive value, it means that additional power is needed; if the power load is a negative value, it means that the load is redundant. The power load can effectively guide the load scheduling strategy and avoid an imbalance in power supply and demand.

[0068] In the specific implementation, first, for each battery pack, the product of the remaining power in the battery state corresponding to the battery pack and the charging speed can be used as the power storage ratio of the battery pack. The power storage ratio of each battery pack can be obtained in the above manner, and the product of the average of all power storage ratios and the stored power in the charging and swapping cabinet can be used as the power storage capacity of the charging and swapping cabinet. The power storage capacity is an indicator to measure the available power in the charging and swapping cabinet; then, the power of each battery swap can be obtained from the historical battery swapping record of the charging and swapping cabinet, and all battery swapping powers can be used as the single battery swapping power of the charging and swapping cabinet, so that the product of the single battery swapping power and the opening frequency can be used as the power demand of the charging and swapping cabinet. The power demand refers to the total amount of power required by the charging and swapping cabinet to meet the battery swapping service; finally, the difference between the power demand and the power storage capacity can be used as the power load of the charging and swapping cabinet.

[0069] In some embodiments, the charging priority of each battery pack in the charging and swapping cabinet can be determined according to the power load and the battery swapping frequency of each battery pack by using the following steps:

[0070] For each battery pack in the charging and swapping cabinet, the initial priority of the battery pack is determined according to the battery swapping frequency of the battery pack;

[0071] Determining a priority correction value of the battery pack according to the power load and the remaining power of the battery pack;

[0072] The charging priority of the battery pack is determined according to the priority correction value and the initial priority, and then the charging priority of each battery pack in the charging and swapping cabinet is obtained.

[0073] It should be noted that in the present application, the charging priority is a quantitative indicator used to quantify the scheduling priority of the battery group in the charging and swapping cabinet in multi-way power distribution. The smaller the charging priority value, the higher the charging priority, and charging should be given priority. In specific implementation, first, for each battery group in the charging and swapping cabinet, the inverse of the battery pack's battery swapping frequency can be used as the initial priority of the battery group. The initial priority is a parameter for preliminarily calibrating the basic order of charging scheduling. Then, the product of the remaining power of the battery group divided by the total power of the battery group and the power load can be used as the priority correction value of the battery group. The priority correction value indicates the degree of influence of the real-time supply and demand balance on the charging priority of the battery group. Finally, the sum of the initial priority and the priority correction value can be used as the charging priority of the battery group. The charging priority of each battery group in the charging and swapping cabinet can be obtained in the above manner.

[0074] In step 103, the load characteristics of the charging and swapping cabinet are extracted from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and the load balance of the charging power distribution in the charging and swapping cabinet is determined according to the load characteristics and the multi-channel charging power.

[0075] In some embodiments, based on the mapping relationship between the battery packs in the charging and swapping cabinet, the load characteristics of the charging and swapping cabinet are extracted from all battery states, referring to Figure 3 The figure is a schematic diagram of a process for determining load characteristics in some embodiments of the present application. In this embodiment, the load characteristics can be determined by the following steps:

[0076] In step 1031, a mapping relationship between each battery pack in the charging and swapping cabinet is constructed based on a state mapping mechanism of a neural network;

[0077] In step 1032, mapping characteristics of the load in the charging and swapping cabinet are determined according to all mapping relationships;

[0078] In step 1033, the load characteristics of the charging and swapping cabinet are extracted from all battery states through the mapping characteristics.

[0079] It should be noted that the state mapping mechanism is a mathematical model that associates the operating state of a battery pack with its load characteristics through deep learning technology. In specific implementation, the mapping relationship between the battery packs in the charging and swapping cabinet can be constructed based on the state mapping mechanism of the neural network. The following methods can be used to implement the mapping relationship, namely: collect the charging performance data of each battery pack (for example, voltage, current, temperature, etc.), normalize all the charging performance data, generate an input feature vector, and use the state mapping mechanism to design a multi-layer feedforward neural network, in which the input layer corresponds to the battery state characteristics, the output layer corresponds to the target load distribution factor, and the hidden layer captures each battery pack through a nonlinear activation function (for example, ReLU). The mapping relationship between battery groups is calculated by using the historical running data set and adjusting the network parameters through the back propagation algorithm (for example, Adam optimizer) to minimize the mean square error between the predicted load and the actual load in each battery group. The trained network is then deployed in the mapping model, and the current battery status of each battery group is input into the mapping model to dynamically generate a mapping relationship matrix between each battery group. The similarity values ​​between each battery group in the mapping relationship matrix can be used as the mapping relationship between the corresponding battery groups in the battery swap cabinet, so as to obtain the mapping relationship between each battery group in the charging and swap cabinet, where the similarity value represents the sequence point distance between each battery group in the mapping relationship matrix.

[0080] In specific implementation, the mapping characteristics of the load in the charging and swapping cabinet can be determined based on all mapping relationships in the following manner, namely: calculate the sum of the mean and mode of the similarity values ​​in each mapping relationship and divide it by 2 as the load concentration value in the charging and swapping cabinet, and use the standard deviation of the similarity values ​​in each mapping relationship as the load fluctuation amount in the charging and swapping cabinet. The set of load concentration values ​​and load fluctuation amounts can be used as the mapping characteristics of the load in the charging and swapping cabinet, wherein the mapping characteristics are characteristics used to describe the temporal and spatial distribution law of the load in the charging and swapping cabinet.

[0081] It should be noted that in the present application, the load characteristic is a state characterization of each battery pack in the charging and swapping cabinet under load conditions; in specific implementation, the load characteristics of the charging and swapping cabinet can be extracted from all battery states through the mapping characteristics in the following manner, namely: the product of the load concentration value and the load fluctuation amount in the mapping characteristics can be used as the adjustment value of the power threshold, and the power load threshold of the charging and swapping cabinet is obtained from the central console of the charging and swapping cabinet. For each battery pack, the absolute value of the difference between the input power and the power load threshold in the battery state of the battery pack can be used as the load difference of the battery pack. The load difference of each battery pack can be obtained in the above manner, and the set of all load differences can be used as the load characteristic of the charging and swapping cabinet.

[0082] In some embodiments, the load balance of charging power distribution in the charging and swapping cabinet can be determined according to the load characteristics and the multi-channel charging power by using the following steps:

[0083] Build an intelligent dispatching mechanism for power distribution in charging and swapping cabinets based on machine learning algorithms;

[0084] Determine a load scheduling strategy in the intelligent scheduling mechanism according to the load characteristics and the multi-channel charging power;

[0085] The load of the power distribution in the load scheduling strategy is balanced and adjusted through the intelligent scheduling mechanism to obtain the load balance degree of the charging power distribution in the charging and swapping cabinet.

[0086] It should be noted that in the present application, load balancing refers to the uniformity of power distribution among different battery packs under the current load scheduling strategy; the intelligent scheduling mechanism is a mechanism for generating a power allocation strategy for the charging process of the battery pack in the charging and swapping cabinet. The intelligent scheduling mechanism can respond to load changes in real time, dynamically adjust the charging power, achieve load balancing, and continuously optimize the model through a feedback mechanism to improve the overall scheduling effect; the load scheduling strategy refers to the strategy for allocating charging power in the intelligent scheduling mechanism.

[0087] In the specific implementation, first, initialize the scheduling model in the intelligent scheduling mechanism, select a suitable machine learning algorithm (for example: support vector machine) to design the structure of the scheduling model and set the hyperparameters, initialize the scheduling weights in the scheduling model, and during the training process, perform model training of the scheduling model through forward propagation and back propagation, use the loss function to optimize the weights, and adjust the hyperparameters on the validation set to avoid overfitting. The evaluation phase is performed on the test set, and the relevant performance indicators are used to evaluate and optimize the model. After the training is completed, the model is integrated and deployed into the intelligent scheduling mechanism, and the intelligent scheduling mechanism for power distribution in the charging and swapping cabinet can be completed. Then, the load scheduling strategy is used as the load scheduling strategy in the intelligent scheduling mechanism; finally, the intelligent scheduling mechanism is used to balance the load of the power distribution in the load scheduling strategy, so that the quantified value of the load evaluation result in the intelligent scheduling mechanism is used as the load balancing degree of the charging power distribution in the charging and swapping cabinet.

[0088] Preferably, in the above embodiment, the load scheduling strategy in the intelligent scheduling mechanism is determined according to the load characteristics and the multi-channel charging power, which can be implemented by the following steps:

[0089] Initialize a power scheduling model;

[0090] Determining a load function of the power scheduling model according to the load characteristics;

[0091] Using the multi-channel charging power as the input power of the power scheduling model;

[0092] This power scheduling model is used to determine the load scheduling strategy for charging power in the charging and swapping cabinet.

[0093] In the specific implementation, first, a power scheduling model can be initialized using a machine learning algorithm (for example, a neural network). The initialization process includes setting the basic structure, parameters, and initial state of the power scheduling model. Secondly, a linear programming function in the power scheduling model is obtained, and all load differences in the load characteristics are used as learning objects of the linear programming function. The linear programming function is trained using the power scheduling model, and the trained linear programming function can be used as the load function of the power scheduling model. Then, multiple charging powers are used as input powers of the power scheduling model. Finally, the power scheduling model is used to generate a scheduling plan for the charging power in the charging and swapping cabinet, and the scheduling plan can be used as a load scheduling strategy for the charging power in the charging and swapping cabinet.

[0094] It should be noted that in this application, the load scheduling strategy is a control instruction that guides the allocation and adjustment of charging power; the power scheduling model is a system based on machine learning, which is used to dynamically determine the distribution of power during battery charging based on real-time and historical data. The purpose of the power scheduling model is to optimize the energy distribution during the charging process and ensure the charging efficiency of each battery pack in the charging and swapping cabinet and the stable operation of the system; the load function refers to a mathematical expression used to describe and quantify the charging load.

[0095] In step 104, the charging power of each battery pack in the charging and swapping cabinet in the subsequent charging process is dynamically adjusted according to the load balancing degree and the charging priority of each battery pack.

[0096] In some embodiments, dynamically adjusting the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack can be achieved by the following steps:

[0097] When the load balancing degree is lower than the preset balancing threshold, the charging priority of each battery group is used as the allocation priority level of the corresponding battery group in the charging and swapping cabinet in the subsequent charging process, so as to complete the dynamic adjustment of the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process.

[0098] In the present application, the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process is adaptively adjusted according to the load balancing degree and the charging priority of each battery group; first, the reasonable determination of the charging priority can ensure the high efficiency of power distribution. For example, batteries with poor performance or low remaining power will be given a higher charging priority to avoid excessive discharge of the battery and maintain the battery in a healthy state. The adaptive adjustment mechanism can flexibly allocate power according to the needs of different batteries, avoid overheating or energy waste of a single battery group due to excessive charging, optimize the overall power distribution, and thus improve the overall efficiency of the charging process; then, the load balancing degree is a measure of the uniformity of power distribution of all battery groups in the charging and swapping cabinet during the charging process. Excessive load balancing means that some battery groups may be in an overcharged state due to receiving too much charging power, affecting the health of the battery; while too low load balancing may cause some battery groups to be undercharged, affecting the charging efficiency. The determination of the load balancing degree can help ensure that each battery group receives appropriate power distribution during its charging process. For example, when the battery condition is poor or the remaining power of a battery pack is low, the scheduling mechanism will prioritize allocating more power. When the load is high, the intelligent scheduling mechanism will automatically adjust the power distribution to avoid overload or power waste. The charging and swapping cabinet can adaptively adjust the power distribution according to the real-time changes in load balance, thereby maximizing charging efficiency, reducing power waste, and improving the power distribution efficiency during the charging process. In summary, based on the above solution, the battery condition and the battery swapping frequency can be combined to realize dynamic adjustment of the charging power of the battery pack in the charging and swapping cabinet, thereby improving the power distribution efficiency during the charging process.

[0099] In addition, in another aspect of the present application, in some embodiments, the present application provides an intelligent charging system for a charging and swapping cabinet, referring to Figure 4 , which is a schematic diagram of the structure of the intelligent charging system of the charging and swapping cabinet according to some embodiments of the present application. The intelligent charging system of the charging and swapping cabinet includes: a monitoring module 201, a processing module 202 and an execution module 203, which are respectively described as follows:

[0100] Monitoring module 201, in this application, the monitoring module 201 is mainly used to monitor the battery status of each battery pack in the charging and swapping cabinet under multiple charging powers during the charging process of the charging and swapping cabinet;

[0101] Processing module 202, in this application, the processing module 202 is used to extract the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determine the power load of the charging and swapping cabinet according to the opening frequency and the charging performance in each battery state, and determine the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack;

[0102] It should be noted that the processing module 202 is also used to extract the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and determine the load balance of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power;

[0103] Execution module 203. In the present application, execution module 203 is mainly used to dynamically adjust the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack.

[0104] The above describes in detail the examples of the intelligent charging method and system of the charging and swapping cabinet provided in the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0105] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned intelligent charging method of the charging and swapping cabinet.

[0106] In some embodiments, reference Figure 5 , the dotted line in the figure indicates that the unit or the module is optional. The figure is a structural diagram of a computer device for implementing an intelligent charging method for a charging and swapping cabinet according to an embodiment of the present application. The intelligent charging method for the charging and swapping cabinet described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0107] The processor 301 may be a general-purpose processor or a special-purpose processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data of the software programs. The computer device may also include a communication unit 305 to implement signal input (reception) and output (transmission).

[0108] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other devices.

[0109] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0110] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 performs the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read the data stored in the memory 302, and the data can be stored at the same storage address as the program 304, or the data can be stored at a different storage address from the program 304.

[0111] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of the terminal device.

[0112] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0113] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0114] For example, in some embodiments, the present application also provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned intelligent charging method for the charging and swapping cabinet when executing.

[0115] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0116] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. An intelligent charging method for a charging and swapping cabinet, characterized in that: The steps include: During the charging process of the charging and swapping cabinet, the battery status of each battery pack in the charging and swapping cabinet is monitored under multiple charging powers; Extract the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determine the power load of the charging and swapping cabinet according to the opening frequency and the charging performance in each battery state, and determine the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack; Extracting the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and determining the load balance degree of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power; The charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process is dynamically adjusted according to the load balancing degree and the charging priority of each battery pack.

2. The method according to claim 1, characterized in that The opening frequency of the charging and swapping cabinet and the replacement frequency of each battery pack are extracted from the historical battery replacement records of the charging and swapping cabinet, including: Obtaining the opening interval of each opening of the charging and swapping cabinet from the historical battery swapping record; Determine the opening frequency of the charging and swapping cabinet based on all the opening intervals; For each battery pack, obtaining the replacement interval of each battery replacement of the battery pack from the historical battery replacement record; The replacement frequency of the battery pack is determined based on all replacement intervals, and then the replacement frequency of each battery pack is determined.

3. The method according to claim 1, characterized in that Determining the power load of the charging and swapping cabinet by the opening frequency and the charging performance in each battery state specifically includes: Determine the power storage capacity of the charging and swapping cabinet according to the charging performance of each battery state and the stored power in the charging and swapping cabinet; Determining the power demand of the charging and swapping cabinet according to the opening frequency; The power load of the charging and swapping cabinet is determined by the power demand and the power storage capacity.

4. The method according to claim 1, characterized in that Determining the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack specifically includes: For each battery pack in the charging and swapping cabinet, the initial priority of the battery pack is determined according to the battery swapping frequency of the battery pack; Determining a priority correction value of the battery pack according to the power load and the remaining power of the battery pack; The charging priority of the battery pack is determined according to the priority correction value and the initial priority, and then the charging priority of each battery pack in the charging and swapping cabinet is obtained.

5. The method according to claim 1, characterized in that Based on the mapping relationship between the battery packs in the charging and swapping cabinet, the load characteristics of the charging and swapping cabinet are extracted from all battery states, including: The mapping relationship between each battery pack in the charging and swapping cabinet is constructed based on the state mapping mechanism of the neural network; Determine the mapping characteristics of the load in the charging and swapping cabinet according to all mapping relationships; The load characteristics of the charging and swapping cabinet are extracted from all battery states through the mapping characteristics.

6. The method according to claim 1, characterized in that Determining the load balance of charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power specifically includes: Build an intelligent dispatching mechanism for power distribution in charging and swapping cabinets based on machine learning algorithms; Determine a load scheduling strategy in the intelligent scheduling mechanism according to the load characteristics and the multi-channel charging power; The load of the power distribution in the load scheduling strategy is balanced and adjusted through the intelligent scheduling mechanism to obtain the load balance degree of the charging power distribution in the charging and swapping cabinet.

7. The method according to claim 1, characterized in that Dynamically adjusting the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack specifically includes: When the load balancing degree is lower than the preset balancing threshold, the charging priority of each battery group is used as the allocation priority level of the corresponding battery group in the charging and swapping cabinet in the subsequent charging process, so as to complete the dynamic adjustment of the charging power of each battery group in the charging and swapping cabinet in the subsequent charging process.

8. An intelligent charging system for a charging and swapping cabinet, characterized in that: include: A monitoring module is used to monitor the battery status of each battery pack in the charging and swapping cabinet under multiple charging powers during the charging process of the charging and swapping cabinet; A processing module, used to extract the opening frequency of the charging and swapping cabinet and the battery swapping frequency of each battery pack from the historical battery swapping record of the charging and swapping cabinet, determine the power load of the charging and swapping cabinet according to the opening frequency and the charging performance in each battery state, and determine the charging priority of each battery pack in the charging and swapping cabinet according to the power load and the battery swapping frequency of each battery pack; The processing module is also used to extract the load characteristics of the charging and swapping cabinet from all battery states based on the mapping relationship between the battery groups in the charging and swapping cabinet, and determine the load balance degree of the charging power distribution in the charging and swapping cabinet according to the load characteristics and the multi-channel charging power; The execution module is used to dynamically adjust the charging power of each battery pack in the charging and swapping cabinet during the subsequent charging process according to the load balancing degree and the charging priority of each battery pack.

9. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the intelligent charging method of the charging and swapping cabinet according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the intelligent charging method for the charging and swapping cabinet as described in any one of claims 1 to 7.

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