Intelligent charging control system of battery replacement cabinet based on power safety management

Abstract

The application discloses a power safety management-based intelligent charging control system of a battery replacement cabinet, and relates to the technical field of charging control of the battery replacement cabinet.The system comprises a power distribution network transient electrical parameter high-frequency acquisition unit, a core control unit, a battery replacement cabinet cluster cooperative management and control unit, a warehouse position level charging execution unit and a relay protection unit.The application realizes real-time and accurate perception of the transient operation situation of the power distribution network by arranging the power distribution network transient electrical parameter high-frequency acquisition unit in the charging control system of the battery replacement cabinet.Combining with the built-in power distribution network transient safety margin evaluation model, the safety operation constraint interval of the current power distribution network is quantified.Through the cooperative cooperation of the battery replacement cabinet cluster cooperative management and control unit and the warehouse position level charging execution unit, the multi-level charging management and control strategy suitable for the safety margin of the power distribution network is generated, the charging power and timing of the battery replacement cabinet cluster are adaptively adjusted, and the deep coupling of the charging control of the battery replacement cabinet and the power safety management of the power distribution network is realized.

Description

Technical Field

[0001] This invention relates to the field of battery swapping cabinet charging control technology, specifically to an intelligent charging control system for battery swapping cabinets based on power safety management. Background Technology

[0002] Battery swapping stations, also known as power battery swapping cabinets, are intelligent electrical devices designed for civilian new energy transportation sectors such as two-wheeled electric vehicles and low-speed new energy vehicles. They provide centralized charging, safe storage, and convenient battery swapping services for power batteries. With the rapid popularization of new energy short-distance transportation, battery swapping stations, with their advantages of fast charging speed, strong site adaptability, standardized battery management, and high safety protection level, have become a core component of urban end-point new energy charging systems. They effectively solve the industry pain points of safety hazards and low charging efficiency for civilian power battery users charging themselves, and have significant practical significance and industrial value for promoting the large-scale development of new energy short-distance transportation and improving the urban new energy infrastructure network.

[0003] The intelligent charging control system of the battery swapping cabinet is the core control unit of the cabinet equipment. It undertakes core functions such as matching charging parameters for the power battery, managing the entire charging process, providing electrical safety protection, and allocating equipment power. Its control logic and performance directly determine the charging efficiency, operational stability, equipment reliability, and electrical safety protection capabilities of the battery swapping cabinet. It is the core carrier for ensuring the stable operation of the battery swapping cabinet, accurately adapting to the charging needs of the power battery, and preventing electrical safety accidents. Simultaneously, the intelligent charging control system is also a crucial link connecting the battery swapping cabinet terminal equipment with the urban power distribution network. Its charging scheduling logic and control methods directly affect the operational safety and power quality of the power distribution network at the battery swapping cabinet's connection point, making it a core key to achieving large-scale cluster deployment of battery swapping cabinets and friendly access to the power distribution network.

[0004] However, existing intelligent charging control systems for battery swapping cabinets primarily focus on the power scheduling of the cabinet itself and the matching of charging demands with the power batteries during design and application. They can only achieve conventional steady-state power regulation and basic electrical protection, without deeply coupling the charging control logic with the operation and safety management of the distribution network. They cannot perceive the transient operating status of the distribution network. In scenarios with multiple battery swapping cabinets deployed in clusters and multiple compartments charging in parallel, disordered charging behavior can easily lead to problems such as voltage dips, harmonic distortion, and three-phase imbalance in the distribution network. This not only exacerbates the operating losses of the distribution network and affects the stable operation of other electrical equipment within the distribution network, but in severe cases, it can also cause power safety accidents such as malfunctions of distribution network protection devices and power outages. At the same time, existing demand response modes can only adapt to power regulation under the steady-state operating conditions of the distribution network and cannot cope with the safety risk management under transient fluctuation scenarios of the distribution network. It is difficult to achieve coordinated adaptation between the charging behavior of the battery swapping cabinet and the power safety management of the distribution network. Therefore, developing an intelligent charging control system for battery swapping cabinets based on power safety management is of great significance. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies and provide an intelligent charging control system for battery swapping cabinets based on power safety management. This system can deeply couple the charging control of battery swapping cabinets with the power safety management of the distribution network, realize real-time perception of the transient operating status of the distribution network and safety margin assessment, and generate charging management and control strategies adapted to the safety constraints of the distribution network. This solves the problem in existing technologies where the charging control of battery swapping cabinets is disconnected from the safety management of the distribution network and cannot cope with the transient safety risks of the distribution network. Under the premise of ensuring the safe operation of the distribution network, it takes into account both the charging needs and operating efficiency of the battery swapping cabinets.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent charging control system for battery swapping cabinets based on power safety management. The system includes: a high-frequency acquisition unit for transient electrical parameters of the distribution network, a core control unit, a battery swapping cabinet cluster collaborative management and control unit, a bay-level charging execution unit, and a relay protection unit.

[0007] The high-frequency acquisition unit for transient electrical parameters of the power distribution network is set on the incoming side of the power distribution network of the battery swapping cabinet, electrically connected to the power distribution network bus, and communicatively connected to the core control unit. It is used to acquire transient electrical parameters of the power distribution network side at the access point of the battery swapping cabinet and upload them to the core control unit.

[0008] The core control unit is bidirectionally connected to the other units and has a built-in distribution network transient safety margin assessment model and charging management strategy generation module. It calculates the distribution network transient safety margin based on the received transient electrical parameters and generates a charging management strategy in combination with the charging requirements of the battery swapping cabinet.

[0009] The battery swapping cabinet cluster collaborative management and control unit is connected to the local controller of each battery swapping cabinet within the management and control area. It is used to collect data on the operating status and charging demand of the battery swapping cabinets in the cluster and execute cluster-level charging management and control instructions issued by the core control unit.

[0010] The compartment-level charging execution unit is set up in each charging compartment of the battery swapping cabinet to collect the status data of the power battery in the compartment and execute the compartment-level charging control instructions issued by the core control unit.

[0011] The relay protection unit is bidirectionally connected to the core control unit and includes an incoming line side main protection module, a battery swapping cabinet body protection module, and a compartment branch protection module. It is used to perform hierarchical electrical safety protection throughout the charging cycle and upload fault data to the core control unit.

[0012] Furthermore, the charging control strategy generation module built into the core control unit performs the following operations when generating the charging control strategy:

[0013] Receive the transient safety margin data of the distribution network output by the distribution network transient safety margin assessment model, and extract the power regulation upper limit and safety constraint parameters corresponding to the current operating state of the distribution network;

[0014] The system receives operational status data of each battery swapping cabinet within the cluster from the cluster collaborative management unit and power battery status data from the bay-level charging execution unit. Based on a charging priority quantification calculation formula, it assigns a charging priority to each charging bay. The calculation formula is as follows: ,in Quantify the charging priority value for a single compartment. This refers to the remaining capacity parameter of the power battery. These are parameters related to the health status of the power battery. The charging time parameter for the power battery is determined by fitting orthogonal experimental data of the power battery charging characteristics, with α, β, and γ being priority weight coefficients.

[0015] Orthogonal experiment to select the remaining capacity of the power battery Health status Charging time required As three influencing factors, each factor was set with three levels: SOC factor with three levels of 20%, 50%, and 80%; SOH factor with three levels of 70%, 85%, and 100%; and T_req factor with three levels of 0.5h, 2h, and 4h. The L9(3) model was used. 3 Nine experimental schemes were designed using an orthogonal array. During the experiments, environmental and electrical parameters, such as the charging ambient temperature of 25℃ and the charging voltage at the battery's rated charging voltage, were kept constant. Each experiment was repeated three times, and the average value was taken. The comprehensive charging benefit was used as the evaluation index, which included a weighted score across three dimensions: charging efficiency, battery life degradation rate, and user demand satisfaction. Data fitting employed a multiple linear regression method, using the levels of the three factors as independent variables and the experimentally measured comprehensive charging benefit score as the dependent variable. The regression coefficients were solved using the least squares method, and the initial weight coefficients α, β, and γ were obtained after normalization. The fitting process involved calculating the coefficient of determination R0. 2 To test the goodness of fit, the R-squared value of the final fitted model was determined. 2 A value of 0.972 indicates that the regression equation fits the experimental data well. After fitting, the reliability of the weight coefficients was verified through three sets of validation experiments. The relative errors between the validation experiment results and the model predictions were all less than 3%, proving that the set of weight coefficients can accurately quantify the influence of each factor on charging priority and has the reliability for engineering applications.

[0016] Based on the power regulation upper limit and safety constraint parameters, and combined with the charging priority quantification value of each charging compartment, a three-level charging control strategy is generated at the cluster level, cabinet level, and compartment level, and then distributed to the corresponding execution unit.

[0017] Furthermore, the distribution network transient safety margin assessment model built into the core control unit receives transient electrical parameter data uploaded by the high-frequency acquisition unit of the distribution network transient electrical parameters, extracts four core characteristic parameters from the transient electrical parameters: voltage fluctuation amplitude, harmonic content ratio, three-phase imbalance, and system short-circuit capacity. These four core characteristic parameters are compared with preset distribution network safe operation thresholds to calculate the safety margin sub-values ​​corresponding to each parameter. Based on the transient safety margin weighted calculation formula, the overall transient safety margin of the current distribution network is obtained. The calculation formula is as follows: ,in This represents the overall transient safety margin of the distribution network. Here is the safety margin component value corresponding to the i-th feature parameter. The specific calculation formula is as follows: ,in Let be the measured value of the i-th feature parameter. As a safety benchmark value, This is the maximum permissible deviation value. The weighting coefficient is the value corresponding to the i-th feature parameter. The weighting coefficient is determined by the grey relational analysis method of historical operation data of the distribution network area. The overall transient safety margin value is output to the charging management and control strategy generation module.

[0018] The input-output mapping logic of this model is based on the electrical mechanism that "the degree of deviation of transient parameters from the safety benchmark is negatively correlated with the safety level of the distribution network." First, the measured deviation of a single-dimensional transient parameter is mapped to a safety quantification value in the range of 0-1 using a partial margin formula. The smaller the parameter deviation from the benchmark, the closer the corresponding partial margin is to 1, indicating a higher safety level of the distribution network operation in that dimension. When the parameter deviation exceeds the maximum allowable deviation value, the partial margin returns to zero, indicating that the safety boundary of that dimension has been reached. On this basis, a weighted summation algorithm is used to achieve the fusion quantification of multi-dimensional safety states. The weight coefficient is used to characterize the degree of influence of different transient parameters on the overall transient safety of the distribution network. The larger the weight, the stronger the impact of the parameter fluctuation on the safety of the power grid.

[0019] The model construction comprises two phases: training and calibration, and performance verification. In the training phase, 12 consecutive months of fault-free historical operating data from the target distribution area were selected as the training set. Using the steady-state safe operation of the power grid as a reference benchmark, the correlation between four characteristic parameters and the power grid safety state was calculated using grey relational analysis, and the initial weight coefficients were obtained after normalization. In the verification phase, three months of operating data from the same distribution area, including transient fluctuation scenarios, were selected as the test set. The safety margin output by the model was compared with the actual power grid operating results. By fine-tuning the weight coefficients, the model's accuracy in identifying transient risks reached over 95%, completing model calibration. To assess the model's generalization ability, cross-scenario verification was conducted in three distribution network areas with different load characteristics within the same region. The model output results for each area showed a consistency of no less than 92% with the actual safety state, demonstrating that the model can adapt to the transient safety assessment needs of different distribution areas and possesses good generalization performance.

[0020] Furthermore, the battery swapping cabinet cluster collaborative management and control unit is equipped with an edge computing gateway. The edge computing gateway establishes a bidirectional communication connection with the core control unit via an industrial Ethernet network and establishes a point-to-point communication connection with all local controllers of the battery swapping cabinets within the management and control area via a wireless communication network. The edge computing gateway sends data acquisition instructions to each local controller of the battery swapping cabinet within the management and control area at fixed time intervals, receives equipment operating status, real-time load power, number of available charging bays, and summary data of charging demand for each bay from each local controller, summarizes and organizes all received data, and uploads it to the core control unit in real time.

[0021] Furthermore, the high-frequency acquisition unit for transient electrical parameters of the distribution network performs the following operations when acquiring and processing transient electrical parameters:

[0022] The built-in voltage and current transformers synchronously collect real-time waveform data of the three-phase voltage and three-phase current of the distribution network bus at the access point of the battery swapping cabinet.

[0023] The acquired real-time waveform data is filtered and converted from analog to digital to obtain electrical waveform data in digital format.

[0024] Time-domain and frequency-domain analyses are performed on digital electrical waveform data to extract transient electrical parameter characteristic values ​​corresponding to voltage fluctuations, harmonic distribution, three-phase unbalance, and system short-circuit capacity, which are then uploaded to the core control unit.

[0025] Furthermore, the compartment-level charging execution unit is equipped with a charging power adjustment module and a battery status acquisition submodule. The battery status acquisition submodule establishes a communication connection with the power battery management system in the corresponding compartment, and collects data on the remaining capacity, health status, nominal voltage, rated capacity, charging demand, and body temperature of the power battery through the power battery management system. The collected data is uploaded to the core control unit in real time. The charging power adjustment module is electrically connected to the charging circuit of the corresponding compartment, receives compartment-level charging control commands issued by the core control unit, and adjusts the output power, conduction timing, and impedance matching parameters of the charging circuit.

[0026] Furthermore, throughout the entire cycle of the charging control strategy execution, the core control unit receives updated transient electrical parameter data uploaded by the high-frequency acquisition unit of the distribution network transient electrical parameters at fixed time intervals. It then recalculates the current transient safety margin of the distribution network using the distribution network transient safety margin assessment model. Based on the transient safety margin fluctuation judgment formula, it compares the difference between the old and new transient safety margins. The calculation formula is as follows: ,in This represents the difference in transient safety margin fluctuations. This is the transient safety margin value calculated for the current period. The transient safety margin value calculated in the previous period, and the fluctuation difference. The fluctuation threshold is compared with a preset fluctuation threshold, which is calibrated based on the transient safety operation standards of the distribution network and the charging response characteristics of the battery swapping cabinet cluster. The preset fluctuation threshold first determines the safety margin benchmark range based on the transient safety operation standards of the distribution network, then calculates the initial threshold based on the charging response characteristics of the battery swapping cabinet cluster, and finally obtains the final threshold through statistical calibration using the 3σ principle based on 30 days of fault-free operation history data of the distribution area. When the preset fluctuation threshold is exceeded, the charging control strategy generation module regenerates a charging control strategy that is adapted to the current transient safety margin and sends it to the corresponding execution unit.

[0027] Furthermore, the relay protection unit performs the following operations when executing hierarchical electrical safety protection:

[0028] Through the main protection module on the incoming line side, the protection module of the battery swapping cabinet body, and the protection module of the compartment branch, the real-time voltage and current data of the corresponding circuit are collected synchronously and uploaded to the core control unit.

[0029] Each protection module compares the collected real-time data with the corresponding protection threshold issued by the core control unit. When the real-time data exceeds the protection threshold, the corresponding level of protection action is triggered.

[0030] Each protection module uploads fault data, including the location, type, and timing of the fault, to the core control unit. The core control unit then adjusts the charging management strategy for the corresponding area based on the received fault data.

[0031] Furthermore, the relay protection unit includes an incoming-side main protection module located on the incoming side of the distribution network area to which the battery swapping cabinet cluster is connected, electrically connected to the distribution network busbar; a battery swapping cabinet body protection module located at the main incoming end of each battery swapping cabinet within the control area, electrically connected to the main input circuit of the battery swapping cabinet; and a compartment branch protection module located on the charging branch of each charging compartment of the battery swapping cabinet, electrically connected to the charging input circuit of the corresponding compartment. All three levels of protection modules establish bidirectional communication connections with the core control unit.

[0032] Furthermore, the core control unit establishes a bidirectional communication connection with the high-frequency acquisition unit of transient electrical parameters of the power distribution network, the collaborative management and control unit of the battery swapping cabinet cluster, the warehouse-level charging execution unit, and the relay protection unit through an industrial Ethernet bus. The industrial Ethernet bus uses the TCP / IP communication protocol for data transmission, and all data transmission processes adopt a fixed frame format and data verification mechanism. The core control unit sends heartbeat detection commands to each unit at fixed time intervals to confirm the online operating status of each unit and receives the operating status data and acquisition data returned by each unit.

[0033] Compared with existing technologies, this intelligent charging control system for battery swapping cabinets based on power safety management has the following advantages:

[0034] This invention achieves real-time and accurate perception of the transient operating status of the distribution network by setting up a high-frequency acquisition unit for transient electrical parameters of the distribution network in the charging control system of the battery swapping cabinet. Combined with the built-in transient safety margin assessment model of the distribution network, it quantifies the current safe operation constraint range of the distribution network. Through the coordinated cooperation of the battery swapping cabinet cluster collaborative management and control unit and the warehouse-level charging execution unit, a multi-level charging management and control strategy adapted to the safety margin of the distribution network is generated. The charging power and timing of the battery swapping cabinet cluster are adaptively adjusted, realizing the deep coupling between the charging control of the battery swapping cabinet and the power safety management of the distribution network. This effectively avoids the transient safety risks of the distribution network caused by disorderly charging, while taking into account the charging needs of the power battery, and significantly improves the operational safety of the battery swapping cabinet and the access friendliness of the distribution network.

[0035] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0036] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0037] Figure 1 This is a schematic diagram of the intelligent charging control system for a battery swapping cabinet based on power safety management.

[0038] Figure 2 A flowchart illustrating the workflow of an intelligent charging control system for a battery swapping cabinet based on power safety management.

[0039] Figure 3 This is a flowchart of the charging management strategy generation module when generating charging management strategies. Detailed Implementation

[0040] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0041] This invention provides an intelligent charging control system for battery swapping cabinets based on power safety management. The system consists of five core modules working together: a high-frequency acquisition unit for transient electrical parameters of the distribution network, a core control unit, a battery swapping cabinet cluster collaborative management and control unit, a compartment-level charging execution unit, and a relay protection unit. The high-frequency acquisition unit for transient electrical parameters of the distribution network is deployed on the incoming side of the distribution network of the battery swapping cabinet. It synchronously acquires three-phase voltage and current waveforms through voltage and current transformers. After filtering, analog-to-digital conversion, and time-domain and frequency-domain analysis, it extracts four core transient electrical parameters: voltage fluctuation amplitude, harmonic content ratio, three-phase imbalance, and system short-circuit capacity, and uploads them to the core control unit. The core control unit has a built-in distribution network transient safety margin assessment model and a charging control strategy generation module. Based on the acquired transient electrical parameters, it calculates the overall safety margin of the distribution network. Simultaneously, it integrates the operating status of the battery swapping cabinet cluster and the status data of the power battery. It uses a charging priority quantification formula to divide the charging priority of each compartment, ultimately generating a three-level charging control strategy at the cluster level, single cabinet level, and compartment level. The battery swapping cabinet cluster collaborative management unit completes regional battery swapping cabinet data acquisition and cluster-level command execution through an edge computing gateway. The bay-level charging execution unit realizes single-bay battery status acquisition and charging parameter adjustment. The relay protection unit realizes full-cycle hierarchical electrical safety protection through three-level modules: incoming line main protection, battery swapping cabinet body protection, and bay branch protection. The entire system deeply couples battery swapping cabinet charging control with power distribution network safety management, perceives the transient operating status of the power distribution network in real time, and adaptively adjusts the charging strategy to accurately meet the charging needs of power batteries while ensuring the safe operation of the power distribution network. The following is a detailed description with reference to specific embodiments.

[0042] This embodiment selects a cluster deployment scenario of battery swapping cabinets for two-wheeled electric vehicles in a densely populated residential area of ​​a city center as the application carrier. Twenty commercial battery swapping cabinets are uniformly deployed within the controlled area, each equipped with sixteen independent charging bays. All battery swapping cabinets are centrally connected to the community's public power distribution network. This distribution network simultaneously handles residential and commercial electricity needs, experiencing frequent load fluctuations and high transient safety constraints. Based on this practical application scenario, this embodiment fully implements the entire process of hardware deployment, data acquisition, safety assessment, strategy generation, command execution, and dynamic control of an intelligent charging control system for battery swapping cabinets based on power safety management. It strictly adheres to the system design logic and technical parameter requirements, achieving coordinated management and control of battery swapping cabinet charging and power distribution network safety.

[0043] System hardware deployment and communication link establishment: The first step in system implementation is to complete the hardware installation, electrical connection and communication network establishment of the five core units to ensure the stability and reliability of the physical foundation and data transmission channels for the collaborative work of each unit.

[0044] The high-frequency acquisition unit for transient electrical parameters of the distribution network is installed on the incoming line side of the distribution network area where the battery swapping cabinet cluster is connected. It completes a standardized electrical connection with the distribution network bus. The unit integrates high-precision voltage transformers and current transformers, and can synchronously acquire the real-time waveforms of the three-phase voltage and three-phase current of the distribution network bus at the battery swapping cabinet access point. The acquisition accuracy meets the requirements for microsecond-level transient signal capture. This unit establishes bidirectional communication with the core control unit through industrial Ethernet to ensure that the acquired data is uploaded in real time without delay.

[0045] The core control unit is deployed in the regional battery swapping control center's computer room, serving as the decision-making core of the entire system. It establishes bidirectional communication connections with the high-frequency acquisition unit for transient electrical parameters of the distribution network, the collaborative control unit for the battery swapping cabinet cluster, the hopper-level charging execution unit, and the relay protection unit via an industrial Ethernet bus. The communication process uses the TCP / IP communication protocol, and all data transmission employs a fixed frame format and multiple data verification mechanisms to prevent data loss and transmission errors. The core control unit sends heartbeat detection commands to each unit at a fixed cycle of once per second, monitoring the online operating status of each unit in real time. If a unit is detected to be offline, an alarm signal is immediately issued to ensure the continuity of system operation.

[0046] The battery swapping cabinet cluster collaborative management unit is equipped with an industrial-grade edge computing gateway. This gateway establishes a high-speed communication link with the core control unit via an industrial Ethernet network, and simultaneously establishes point-to-point communication connections with the local controllers of the twenty battery swapping cabinets within the management area via a 4G wireless communication network, ensuring real-time cluster data acquisition and command issuance. The edge computing gateway also features data preprocessing and caching capabilities to address data transmission issues caused by transient fluctuations in wireless communication.

[0047] Each battery swapping cabinet has a dedicated charging unit installed in one of its sixteen charging bays. These units are electrically connected to the charging circuit within each bay and communicate with the battery management system within the bay via a CAN bus, enabling real-time acquisition of battery status data. Each unit is equipped with an independent charging power adjustment module, allowing it to independently adjust charging parameters in response to control commands.

[0048] The relay protection unit adopts a three-level hierarchical deployment. The main protection module on the incoming line side is installed on the incoming line side of the distribution network area and is electrically connected to the distribution network bus, responsible for the electrical safety protection of the entire cluster's incoming line. The battery swapping cabinet body protection module is installed at the main incoming line end of each battery swapping cabinet and is electrically connected to the main input circuit of the battery swapping cabinet, responsible for the overall safety protection of a single battery swapping cabinet. The compartment branch protection module is installed on the charging branch of each charging compartment and is electrically connected to the charging input circuit of the compartment, responsible for the safety protection of the branch of a single compartment. All three levels of protection modules communicate bidirectionally with the core control unit via industrial Ethernet, uploading protection data in real time and receiving protection threshold commands.

[0049] like Figure 1 The diagram shown is a schematic diagram of the overall structure of the system in this embodiment. Each unit is electrically connected to realize power transmission and control, and is connected by communication to realize data interaction and command issuance, forming a complete intelligent charging control closed-loop system.

[0050] High-frequency acquisition and preprocessing of transient electrical parameters of distribution network: After the system starts up, the high-frequency acquisition unit of transient electrical parameters of distribution network immediately enters the real-time acquisition mode, continuously acquires transient operation data of distribution network and completes standardized processing.

[0051] The acquisition unit synchronously acquires real-time waveform data of the three-phase voltage and three-phase current of the distribution network bus at the connection point of the battery swapping cabinet through built-in voltage and current transformers. The sampling frequency is set to 10 kHz to meet the high-frequency acquisition requirements of transient electrical signals. After acquisition, the signal processing module inside the unit performs digital filtering on the raw waveform data to remove invalid signals such as high-frequency noise and electromagnetic interference from the power grid, retaining the true transient electrical waveform. Subsequently, the filtered analog signal undergoes analog-to-digital conversion, converting the continuous analog waveform into discrete digital electrical waveform data.

[0052] After analog-to-digital conversion, the acquisition unit performs joint time-domain and frequency-domain analysis on the digital waveform data. Time-domain analysis extracts voltage fluctuation amplitude and system short-circuit capacity characteristic values, while frequency-domain analysis calculates harmonic content ratio and three-phase imbalance characteristic values. This yields four core transient electrical parameters: voltage fluctuation amplitude, harmonic content ratio, three-phase imbalance, and system short-circuit capacity. All characteristic values ​​are encapsulated and uploaded to the core control unit in real-time via industrial Ethernet, providing accurate raw data for subsequent distribution network transient safety margin assessment.

[0053] Transient safety margin assessment calculation for distribution network: After receiving transient electrical parameter data, the core control unit immediately starts the built-in transient safety margin assessment model for distribution network and carries out quantitative calculation of the safety status of distribution network.

[0054] The evaluation model first compares the four core characteristic parameters with preset safe operation thresholds for the distribution network. These thresholds are calibrated based on national distribution network operation safety standards and historical operation data of the distribution network area in this community. The model then calculates the safety margin sub-value for each parameter. The closer the sub-value is to 1, the higher the safety margin for that parameter, and the more stable the distribution network operation.

[0055] In the specific implementation of this embodiment, the overall transient safety margin of the distribution network is calculated using a weighted summation formula, the specific formula of which is: , in the formula Represents the overall transient safety margin of the distribution network. This represents the safety margin component value corresponding to the i-th feature parameter. This represents the weight coefficient corresponding to the i-th feature parameter.

[0056] The weighting coefficients were determined using grey relational analysis of historical operating data from the distribution network area. Specifically, the quantification method involved collecting nearly twelve months of historical operating data from the distribution network area of ​​this community, including real-time monitoring data on voltage fluctuations, harmonic content, three-phase imbalance, and short-circuit capacity. Using the fault-free and stable operation of the distribution network as a reference sequence and the four characteristic parameters as a comparison sequence, the grey relational analysis model was used to determine the degree of correlation between the four parameters and the transient safety state of the distribution network. The calculated correlation degrees were then normalized to determine the final weighting coefficients for the four parameters: voltage fluctuation amplitude (0.32), harmonic content percentage (0.28), three-phase imbalance (0.25), and system short-circuit capacity (0.15).

[0057] The mapping logic of the above weighted calculation follows the electrical law that transient parameter deviations are negatively correlated with safety levels: the margin of a single parameter is quantified by the relative deviation between the measured value and the safety benchmark value. The smaller the deviation, the closer the margin is to 1, and the higher the operational safety of the corresponding dimension. When the deviation exceeds the maximum allowable deviation, the margin of the parameter becomes zero, indicating that the dimension has exceeded the safe operation boundary. The weighted fusion process of the four parameters is essentially a comprehensive quantification of safety risks in different dimensions. The weight coefficients directly reflect the impact weight of the corresponding parameter fluctuations on the overall transient safety of the distribution network.

[0058] After model calibration, validation was conducted using three months of operational data from the same distribution area. This dataset included 12 typical transient fluctuation scenarios. The model's output safety margin was compared with the actual power grid operating conditions. The model's accuracy in identifying transient risks reached 96.7%, and the average relative error between the calculated safety margin and the actual safety level was 2.1%, validating the model's accuracy in the distribution area. Simultaneously, generalization capability tests were conducted on three distribution networks with different load characteristics within the same area: residential, commercial, and mixed industrial distribution networks. After applying the model, the risk identification accuracy for each distribution network was no less than 93%, demonstrating that the model can adapt to the transient safety assessment needs of different types of distribution networks and possesses good generalization performance.

[0059] After the core control unit completes the overall transient safety margin calculation, it transmits the calculation results to the charging management and control strategy generation module in real time, which serves as the core safety constraint for the subsequent charging strategy generation, ensuring that the charging power regulation is always within the safe operation range of the distribution network.

[0060] Data collection on the status of battery swapping cabinet clusters and power batteries; while conducting safety margin assessments, the system simultaneously collects data on the operating status of battery swapping cabinet clusters and the status of power batteries, providing data support for charging priority allocation.

[0061] The edge computing gateway of the battery swapping cabinet cluster collaborative management unit sends data acquisition commands to the local controllers of twenty battery swapping cabinets within the management area at a fixed interval of five seconds. Upon receiving the command, each local controller immediately transmits back data on equipment operating status, real-time load power, number of available charging bays, and summarized charging demand for each bay. The edge computing gateway aggregates, categorizes, and standardizes all transmitted data, removes redundant data, and uploads it to the core control unit in real time.

[0062] The battery status acquisition submodule of the bay-level charging execution unit continuously communicates with the power battery management system within the corresponding bay to collect key data such as the remaining capacity, health status, nominal voltage, rated capacity, charging demand, and battery temperature of the power battery. The remaining capacity reflects the battery's current charge level, the health status reflects the degree of battery aging, and the charging demand duration reflects the urgency of the user's battery swapping needs. All this data is uploaded to the core control unit in real time via the communication link, ensuring that charging priority is aligned with the actual battery status and user needs.

[0063] Single-bay charging priority quantification calculation: After receiving the battery swapping cabinet cluster and power battery status data, the charging management and control strategy generation module of the core control unit calculates the priority value of each charging bay through a dedicated quantification formula to achieve reasonable allocation of charging resources.

[0064] In the specific implementation of this embodiment, the formula for quantifying the charging priority of a single compartment is as follows: , in the formula This represents the quantified value of charging priority for a single compartment. This represents the remaining capacity parameter of the power battery. Represents the health status parameters of the power battery. This parameter represents the charging time required for the power battery.

[0065] In the formula, α, β, and γ are priority weighting coefficients, which are determined by fitting orthogonal experimental data of power battery charging characteristics.

[0066] This orthogonal experiment selects the remaining capacity of the power battery Health status Charging time required As three experimental factors, each factor was set with three levels: SOC factor with 20%, 50%, and 80% levels; SOH factor with 70%, 85%, and 100% levels; and T_req factor with 0.5h, 2h, and 4h levels. The L9(3) model was used. 3Nine formal experimental schemes were designed using an orthogonal array. The experiments used mass-produced lithium iron phosphate power batteries of the same model compatible with this battery swapping cabinet as the experimental subjects. During the experiments, the charging environment temperature was kept constant at 25℃, the charging voltage was the battery's rated charging voltage, and the ambient humidity was kept constant at 60%RH to eliminate irrelevant variables. Each experiment was repeated three times, and the average of the three experimental data was taken as the final experimental result. The comprehensive charging benefit score was used as the evaluation index, where the comprehensive charging benefit was calculated by weighting three sub-indicators: charging efficiency (weight 40%), battery life degradation rate (weight 35%), and user demand satisfaction (weight 25%).

[0067] Data fitting employed a multiple linear regression method, using the levels of three experimental factors as independent variables and the experimentally measured comprehensive charging benefit score as the dependent variable. The regression coefficients were calculated using the least squares method, and after normalization, initial weight coefficients for α, β, and γ were obtained. The fitting process involved calculating the coefficient of determination R0. 2 To test the goodness of fit, the final coefficient of determination R of the fitted model is determined. 2 The value of 0.972 indicates that the regression equation has a high degree of explanation for the experimental data and a good fit. After fitting, three additional verification experiments were designed to verify the reliability of the weight coefficients. The relative errors between the measured and predicted values ​​of the comprehensive benefits of the verification experiments were 1.8%, 2.3%, and 2.7%, respectively, all less than 3%, proving that the set of weight coefficients can accurately quantify the influence of each factor on charging priority and can support the reasonable division of charging priority. Finally, through fitting and verification, the optimal combination of weight coefficients was obtained, and the values ​​of α, β, and γ were determined to be 0.45, 0.35, and 0.2.

[0068] The core control unit sorts all charging bays according to the calculated priority quantification value. The higher the quantification value, the higher the charging priority and the more likely the bay will be allocated charging resources. This ensures fast charging for low-power and high-health batteries while also taking into account users' emergency battery swapping needs, thus achieving a balance between charging efficiency and user experience.

[0069] The three-level charging management and control strategy generation and instruction issuance module combines the power regulation upper limit and safety constraint parameters extracted from the transient safety margin of the distribution network, as well as the charging priority ranking results of each compartment, to generate three-level charging management and control strategies at the cluster level, single cabinet level, and compartment level.

[0070] The cluster-level charging control strategy regulates the total charging power of the entire cluster of twenty battery swapping cabinets. The core control unit determines the upper limit of the total charging power of the cluster based on the transient safety margin of the distribution network, avoiding overload of the cluster's total power and resulting in excessive load on the distribution network. The single-cabinet-level charging control strategy regulates the charging power allocation of a single battery swapping cabinet. Based on the real-time load of the battery swapping cabinet and the number of available bays, the total power of the cluster is rationally allocated to each battery swapping cabinet. The bay-level charging control strategy regulates the charging parameters of a single bay. Based on the bay priority, the charging power, conduction timing, and impedance matching parameters are determined. High-priority bays use high-power fast charging, while low-priority bays use low-power orderly charging.

[0071] After the strategy is generated, the core control unit distributes the three-level strategies to the corresponding execution units via industrial Ethernet. The cluster-level strategy is distributed to the battery swapping cabinet cluster collaborative management and control unit, and the single cabinet-level and warehouse-level strategies are distributed to the warehouse-level charging execution units of each battery swapping cabinet.

[0072] like Figure 3 The diagram shows the complete process of generating charging control strategies in this embodiment, from receiving distribution network safety margin data and extracting power constraints to collecting battery status data and calculating priorities, and finally completing the generation and distribution of three-level strategies, forming a standardized strategy generation logic.

[0073] Charging control strategy execution and hierarchical relay protection: After receiving the control strategy, each execution unit immediately starts the charging execution operation, while the relay protection unit provides hierarchical electrical safety protection throughout the process.

[0074] After receiving the cluster-level policy, the battery swapping cabinet cluster collaborative management unit issues power regulation commands to the local controllers of the twenty battery swapping cabinets, strictly controlling the total charging power of the cluster to not exceed the safety limit of the distribution network. After receiving the bay-level policy, the bay-level charging execution unit adjusts the output power, conduction timing, and impedance matching parameters of the corresponding bay-level charging circuit through the charging power adjustment module, and starts the charging process according to priority order to ensure that the charging process conforms to the battery characteristics and distribution network constraints.

[0075] The relay protection unit simultaneously enters full-cycle protection mode. The incoming line main protection module, the swapping cabinet body protection module, and the compartment branch protection module respectively collect real-time voltage and current data of their respective circuits and upload them to the core control unit. Each protection module compares the real-time data with the protection threshold issued by the core control unit. When the real-time data exceeds the protection threshold, the corresponding level of protection action is immediately triggered: the incoming line module triggers the main switch to cut off power, the body module triggers the single cabinet to cut off power, and the branch module triggers the single compartment to cut off power. Fault data such as fault location, fault type, and action time are simultaneously uploaded to the core control unit. The core control unit adjusts the charging control strategy in real time based on the fault information, cutting off the charging circuit in the fault area to prevent the fault from escalating.

[0076] like Figure 2 The diagram shows the overall system workflow in this embodiment. From the acquisition of transient parameters and equipment status data of the distribution network, to safety margin assessment and strategy generation, and then to charging execution and safety protection, a complete operating loop is formed to ensure the stable and reliable operation of the system.

[0077] The transient safety margin is dynamically updated and the strategy is adaptively adjusted. Throughout the entire cycle of the charging control strategy execution, the core control unit continuously monitors the transient operating status of the distribution network and adjusts the charging strategy in real time according to the grid fluctuations to achieve adaptive control.

[0078] The core control unit receives updated transient electrical parameter data uploaded by the high-frequency acquisition unit of the distribution network transient electrical parameters at fixed intervals of once every ten seconds, and recalculates the current transient safety margin of the distribution network through the distribution network transient safety margin assessment model.

[0079] In the specific implementation of this embodiment, the transient safety margin fluctuation difference is calculated using the absolute value formula, which is as follows: , in the formula Represents the difference in transient safety margin fluctuations. This represents the transient safety margin value calculated for the current period. This represents the transient safety margin value calculated in the previous period.

[0080] The preset fluctuation threshold is calibrated based on the transient safety operation standards of the distribution network and the charging response characteristics of the battery swapping cabinet cluster. In this embodiment, the preset fluctuation threshold is set to 0.1. The core control unit compares the calculated fluctuation difference with the preset threshold. When the fluctuation difference exceeds the preset threshold, it indicates that the transient safety state of the distribution network has changed significantly. The charging control strategy generation module immediately regenerates a three-level charging control strategy adapted to the current safety margin and sends it to the corresponding execution unit to complete the strategy update. If the safety margin of the distribution network increases, the system appropriately increases the charging power; if the safety margin decreases, the system reduces the charging power or adjusts the charging sequence, always maintaining a high degree of adaptation between charging control and the safety state of the distribution network.

[0081] Meanwhile, if the core control unit receives fault data uploaded by the relay protection unit, it will immediately adjust the charging strategy of the corresponding area according to the fault location and type, suspend the charging of the faulty equipment, ensure the normal operation of the non-faulty area, and resume charging after the fault is cleared, so as to minimize the impact of the fault on the battery swapping service.

[0082] In summary, this embodiment, based on the actual application scenario of battery swapping cabinet clusters in urban central districts, fully realizes the entire process of intelligent charging control system for battery swapping cabinets based on power safety management. The system achieves real-time and accurate perception of the transient operating status of the distribution network through a high-frequency acquisition unit for transient electrical parameters of the distribution network. It quantifies the safe operation constraint range of the distribution network based on a transient safety margin assessment model, achieves reasonable allocation of charging resources through quantitative calculation of charging priorities, and finally generates a three-level charging management strategy that enables adaptive control of the charging power and timing of the battery swapping cabinet cluster. A three-level relay protection unit provides hierarchical safety protection throughout the charging cycle, and a dynamic strategy adjustment mechanism ensures that the system always adapts to the real-time safety status of the distribution network. After implementation, this embodiment completely solves the problems of voltage sag, harmonic distortion, and three-phase imbalance in the distribution network caused by disordered charging in traditional battery swapping cabinets, effectively reducing distribution network operating losses and avoiding power safety accidents such as distribution network protection malfunctions and power outages. While ensuring the safe operation of the power distribution network, the system also takes into account the charging efficiency and service life of the power battery, improves the operational stability of the battery swapping cabinet and the user's battery swapping experience, and provides a mature and feasible technical solution for the large-scale cluster deployment of battery swapping cabinets and friendly access to the power distribution network, fully demonstrating the practicality, safety and advanced nature of the technical solution of this invention.

[0083] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart charging control system for battery swapping cabinets based on power safety management, characterized in that: The system includes: a high-frequency acquisition unit for transient electrical parameters of the power distribution network, a core control unit, a cluster collaborative management and control unit for battery swapping cabinets, a warehouse-level charging execution unit, and a relay protection unit; The high-frequency acquisition unit for transient electrical parameters of the power distribution network is used to acquire transient electrical parameters of the power distribution network side at the access point of the battery swapping cabinet and upload them to the core control unit; The core control unit has a built-in distribution network transient safety margin assessment model and a charging management and control strategy generation module. It calculates the distribution network transient safety margin based on the received transient electrical parameters and generates a charging management and control strategy in combination with the charging requirements of the battery swapping cabinet. The battery swapping cabinet cluster collaborative management and control unit is used to collect data on the operating status and charging demand of the battery swapping cabinets within the cluster, and execute cluster-level charging management and control instructions issued by the core control unit. The compartment-level charging execution unit is used to collect the status data of the power battery in the compartment and execute the compartment-level charging control instructions issued by the core control unit. The relay protection unit includes an incoming line-side main protection module, a battery swapping cabinet body protection module, and a compartment branch protection module, which are used to perform hierarchical electrical safety protection throughout the charging cycle and upload fault data to the core control unit.

2. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The charging control strategy generation module built into the core control unit performs the following operations when generating the charging control strategy: Receive the transient safety margin data of the distribution network output by the distribution network transient safety margin assessment model, and extract the power regulation upper limit and safety constraint parameters corresponding to the current operating state of the distribution network; The system receives operational status data of each battery swapping cabinet within the cluster from the cluster collaborative management unit and power battery status data from the bay-level charging execution unit. Based on a charging priority quantification calculation formula, it assigns a charging priority to each charging bay. The calculation formula is as follows: ,in Quantify the charging priority value for a single bay. This refers to the remaining capacity parameter of the power battery. These are parameters related to the health status of the power battery. The charging time parameter for the power battery is defined by α, β, and γ, which are priority weighting coefficients. Based on the power regulation upper limit and safety constraint parameters, and combined with the charging priority quantification value of each charging compartment, a three-level charging control strategy is generated at the cluster level, cabinet level, and compartment level, and then distributed to the corresponding execution unit.

3. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The core control unit's built-in distribution network transient safety margin assessment model receives transient electrical parameter data uploaded by the high-frequency acquisition unit of the distribution network transient electrical parameters. It extracts four core characteristic parameters from the transient electrical parameters: voltage fluctuation amplitude, harmonic content ratio, three-phase imbalance, and system short-circuit capacity. These four core characteristic parameters are compared with preset distribution network safe operation thresholds to calculate the corresponding safety margin sub-values. Based on the weighted transient safety margin calculation formula, the overall transient safety margin of the current distribution network is obtained. The calculation formula is as follows: ,in This represents the overall transient safety margin of the distribution network. This represents the safety margin component value corresponding to the i-th feature parameter. The weighting coefficient is the value corresponding to the i-th feature parameter. The weighting coefficient is determined by the grey relational analysis method of historical operation data of the distribution network area. The overall transient safety margin value is output to the charging management and control strategy generation module.

4. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The battery swapping cabinet cluster collaborative management and control unit is equipped with an edge computing gateway. The edge computing gateway establishes a bidirectional communication connection with the core control unit through an industrial Ethernet network and establishes a point-to-point communication connection with the local controllers of all battery swapping cabinets within the management and control area through a wireless communication network. The edge computing gateway sends data acquisition instructions to each local controller of the battery swapping cabinet within the management and control area at fixed time intervals, receives equipment operating status, real-time load power, number of available charging bays, and summary data of charging demand for each bay from each local controller, summarizes and organizes all received data, and uploads it to the core control unit in real time.

5. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The high-frequency acquisition unit for transient electrical parameters of the distribution network performs the following operations when acquiring and processing transient electrical parameters: The built-in voltage and current transformers synchronously collect real-time waveform data of the three-phase voltage and three-phase current of the distribution network bus at the access point of the battery swapping cabinet. The acquired real-time waveform data is filtered and converted from analog to digital to obtain electrical waveform data in digital format. Time-domain and frequency-domain analyses are performed on digital electrical waveform data to extract transient electrical parameter characteristic values ​​corresponding to voltage fluctuations, harmonic distribution, three-phase unbalance, and system short-circuit capacity, which are then uploaded to the core control unit.

6. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The compartment-level charging execution unit is equipped with a charging power adjustment module and a battery status acquisition submodule. The battery status acquisition submodule establishes a communication connection with the power battery management system in the corresponding compartment, and collects data on the remaining capacity, health status, nominal voltage, rated capacity, charging demand, and body temperature of the power battery through the power battery management system. The collected data is uploaded to the core control unit in real time. The charging power adjustment module is electrically connected to the charging circuit of the corresponding compartment, receives compartment-level charging control commands issued by the core control unit, and adjusts the output power, conduction timing, and impedance matching parameters of the charging circuit.

7. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, Throughout the entire cycle of the charging control strategy execution, the core control unit receives updated transient electrical parameter data uploaded by the high-frequency acquisition unit of the distribution network transient electrical parameters at fixed time intervals. It then recalculates the current transient safety margin of the distribution network using the distribution network transient safety margin assessment model. Based on the transient safety margin fluctuation judgment formula, it compares the difference between the old and new transient safety margins. The calculation formula is as follows: ,in This represents the difference in transient safety margin fluctuations. This is the transient safety margin value calculated for the current period. The transient safety margin value calculated in the previous period, and the fluctuation difference. The data is compared with a preset fluctuation threshold, which is calibrated based on the transient safety operation standards of the distribution network and the charging response characteristics of the battery swapping cabinet cluster. The fluctuation difference is within a certain range. When the preset fluctuation threshold is exceeded, the charging control strategy generation module regenerates a charging control strategy that is adapted to the current transient safety margin and sends it to the corresponding execution unit.

8. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The relay protection unit performs the following operations when executing hierarchical electrical safety protection: Through the main protection module on the incoming line side, the protection module of the battery swapping cabinet body, and the protection module of the compartment branch, the real-time voltage and current data of the corresponding circuit are collected synchronously and uploaded to the core control unit. Each protection module compares the collected real-time data with the corresponding protection threshold issued by the core control unit. When the real-time data exceeds the protection threshold, the corresponding level of protection action is triggered. Each protection module uploads fault data, including the location, type, and timing of the fault, to the core control unit. The core control unit then adjusts the charging management strategy for the corresponding area based on the received fault data.

9. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The relay protection unit includes an incoming-side main protection module located on the incoming side of the distribution network area to which the battery swapping cabinet cluster is connected, electrically connected to the distribution network busbar. The battery swapping cabinet body protection module is correspondingly located at the main incoming end of each battery swapping cabinet within the control area, electrically connected to the main input circuit of the battery swapping cabinet. The compartment branch protection module is correspondingly located on the charging branch of each charging compartment of the battery swapping cabinet, electrically connected to the charging input circuit of the corresponding compartment. All three levels of protection modules establish bidirectional communication connections with the core control unit.

10. The intelligent charging control system for battery swapping cabinets based on power safety management according to claim 1, characterized in that, The core control unit establishes a bidirectional communication connection with the high-frequency acquisition unit of transient electrical parameters of the power distribution network, the collaborative management and control unit of the battery swapping cabinet cluster, the warehouse-level charging execution unit, and the relay protection unit through an industrial Ethernet bus. The industrial Ethernet bus uses the TCP / IP communication protocol for data transmission. All data transmission processes adopt a fixed frame format and data verification mechanism. The core control unit sends heartbeat detection commands to each unit at fixed time intervals to confirm the online operating status of each unit and receive the operating status data and acquisition data returned by each unit.

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