Marine container-type battery power supply detection system and method

Abstract

The application discloses a marine container type storage battery power supply detection system and method, relates to the technical field of power supply detection systems, and constructs a start-stop frequency statistical graph and a load mutation mode model. The start-stop frequency statistical graph and the load mutation mode model serve as a dynamic input source. After the current scene is determined according to the dynamic input source, whether the rapid discharge capacity detection process needs to be started is analyzed. If the rapid discharge capacity detection process needs to be started, a storage battery power supply detection strategy is automatically generated based on the current scene. After the storage battery power supply is detected through the detection strategy, whether the battery power supply has a discharge voltage drop is identified. If the battery power supply has the discharge voltage drop, cumulative-variation analysis is performed on the discharge voltage drop, the voltage drop severity of the battery power supply is judged, and the detection process is adaptively adjusted according to the judgment result. The detection system dynamically adjusts the detection sampling frequency, the data processing period and the discrimination strategy according to the drop severity, and effectively improves the operation and maintenance efficiency, the risk suppression ability, the dynamic response and the detection timeliness of the detection system.

Description

Technical Field

[0001] This invention relates to the field of power supply detection system technology, and specifically to a marine containerized battery power supply detection system and method. Background Technology

[0002] In modern marine power systems, batteries are not only used to start the main engine, provide electric propulsion and emergency power, but also play a significant role in the stable operation and safety of the power system. With the rise of new energy ships, pure electric ships and hybrid ships, the capacity of batteries is constantly increasing and the system structure is becoming more complex, which puts forward higher requirements for the operational safety, performance monitoring and life assessment of batteries.

[0003] The existing technology has the following shortcomings:

[0004] 1. Most existing systems rely on fixed periods or static voltage to determine the timing of detection, without considering the effect of actual load disturbances on voltage response. This results in detection often failing to be triggered at the most critical load impact moment, leading to poor detection timeliness and performance evaluation delays.

[0005] 2. The detection process is a fixed procedure and cannot automatically adjust the detection strategy according to the actual operating status of the ship (such as changes in start-stop frequency, external temperature, and battery aging status). This may result in waste of resources and missed critical hazard identification windows due to delayed response.

[0006] Based on this, this application proposes a marine containerized battery power supply testing system and method, which dynamically adjusts the testing sampling frequency, data processing cycle and discrimination strategy according to the severity of the drop, effectively improving the operation and maintenance efficiency, risk suppression capability, dynamic responsiveness and testing timeliness of the testing system. Summary of the Invention

[0007] The purpose of this invention is to provide a marine containerized battery power detection system and method to address the shortcomings of the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for testing the power supply of a marine containerized battery, the method comprising the following steps:

[0009] After the detection system constructs a start-stop frequency statistical chart and a load change mode model, it determines the current scenario and analyzes whether the discharge capability detection process needs to be started based on the current scenario determination results.

[0010] If necessary, a battery power detection strategy can be automatically generated based on the current scenario, and after detecting the battery power through the detection strategy, it can be identified whether there is a drop in the discharge voltage of the battery power.

[0011] If present, perform cumulative-variable analysis on the discharge voltage drop to determine the severity of the battery power supply voltage drop, and adaptively adjust the detection process based on the determination results.

[0012] In a preferred embodiment, cumulative-variance analysis is performed on the discharge voltage drop to determine the severity of the voltage drop in the battery power supply, including the following steps:

[0013] like To determine if a discharge voltage drop exists and calculate the drop rate, the expression is: ;

[0014] in, For the rate of fall, The voltage at which the voltage drops to the starting point. The voltage at the lowest voltage point. As the starting point of the fall, This is the lowest voltage point, and the rate represents the magnitude of the voltage drop per unit time.

[0015] Integrating the drop rate during the drop process, the total voltage loss amplitude is obtained, expressed as: In the formula, This represents the total voltage loss amplitude. For the detection duration, Let be the rate of fall at time t;

[0016] The formula for calculating the drop fluctuation amplitude is: In the formula, The fluctuation range is the value of the drop. For the number of monitoring points, Let be the fall rate at the i-th monitoring point. The average rate of fall;

[0017] If the total voltage drop amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is less than or equal to the fluctuation threshold, the analysis indicates that the voltage drop is severe within the detection period.

[0018] If the total voltage loss amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is greater than the fluctuation threshold, the analysis indicates that the voltage drop is moderate within the detection time.

[0019] If the total voltage loss amplitude is less than the loss threshold, the analysis indicates that the voltage drop is good within the detection period.

[0020] In a preferred embodiment, identifying whether there is a discharge voltage drop in the battery power supply includes the following steps:

[0021] During the execution of the detection strategy, voltage data at the battery output terminal during the discharge phase is continuously collected at a preset sampling frequency. The collected data is recorded as follows: ,in, Indicates the first Each sampling time point To determine the total number of samples within the detection period, iterate through all voltage data and determine if there exists a voltage value at any point in time. satisfy: If the condition is met, it is marked as a voltage drop; otherwise, it is judged as normal discharge. This is the discharge voltage drop threshold.

[0022] In a preferred embodiment, the current scenario is determined, and based on the determination result, it is analyzed whether the discharge capability detection process needs to be initiated, including the following steps:

[0023] Risk level is calculated based on start / stop frequency and load mutation intensity index. Start / stop frequency This refers to the number of times a ship's electrical system starts and stops per unit time, and the load mutation intensity index. It reflects the degree of change in current retention rate during instantaneous start-up and shutdown of the main power system. After standardizing the start-up and shutdown frequency and load change intensity index, the risk score is calculated through the risk scoring function.

[0024] The obtained risk score is compared with a preset first scoring threshold and a second scoring threshold, wherein the first scoring threshold is less than the second scoring threshold;

[0025] If the risk score is less than or equal to the first scoring threshold, the current scenario is classified as a low-risk scenario. If the risk score is greater than the first scoring threshold and less than or equal to the second scoring threshold, the current scenario is classified as a medium-risk scenario. If the risk score is greater than the second scoring threshold, the current scenario is classified as a high-risk scenario.

[0026] In a preferred embodiment, determining the current scenario and analyzing whether the discharge capability detection process needs to be initiated based on the current scenario determination result further includes the following steps:

[0027] The start-stop frequency is obtained by counting the number of starts and stops per unit time in the time series.

[0028] After monitoring the power change rate during each start-up and shutdown using a load mutation mode model, the load mutation intensity index is calculated.

[0029] Based on start / stop frequency and load mutation index, the scenario levels are divided as follows:

[0030] Low-risk scenarios: If The rapid discharge capability testing process is not initiated.

[0031] Medium-risk scenario: If Regularly initiate the rapid discharge capability testing process;

[0032] High-risk scenarios: Initiate the rapid discharge capability testing process;

[0033] in, For start / stop frequency, This is the load mutation intensity index. For frequency threshold, This is the exponential threshold.

[0034] In a preferred embodiment, the start / stop frequency is obtained by counting the number of starts and stops per unit time period from the time series, expressed as: ,in, For start / stop frequency, For statistical time windows The total number of starts and stops recorded internally; This represents the statistical time window.

[0035] In a preferred embodiment, after monitoring the power change rate during each start-up and shutdown using a load mutation mode model, the load mutation intensity index is calculated, expressed as: In the formula, This is the load mutation intensity index. For statistical time windows The total number of start and stop records. This represents the rate of power change during the i-th start-stop.

[0036] In a preferred embodiment, constructing a load mutation mode model includes the following steps:

[0037] The detection system collects voltage, current, active power, and reactive power data from the main power system.

[0038] By recording the ship's start-stop control signals, the system automatically determines the timing of each start-up or stop action and sets an analysis time window to capture the evolution trajectory before and after load disturbances. The time window is as follows: ,in: For start / stop event timestamps This is the pre-analysis period before start-up and shutdown. The load response period after start-up and shutdown;

[0039] The rate characteristics of load change are extracted from the power curve to reflect the intensity of load abrupt changes per unit time, generating a load abrupt change mode model, expressed as:

[0040] ,in: Indicates the rate of change of power. For time intervals, This refers to the instantaneous active power.

[0041] In a preferred embodiment, constructing a start-stop frequency statistics chart includes the following steps:

[0042] Record a timestamp for each start-up and stop operation, labeled as start time or stop time, and include the corresponding status switching flags, including power on / off status and main load on / off status.

[0043] The number of start-stop events is counted within a fixed or sliding time window. The start-stop events within each time window are statistically analyzed, and the number of start-stop events, the number of stop-start events, and the start-stop ratio within that time window are calculated to form frequency statistics.

[0044] A start-stop frequency statistics chart is drawn based on frequency statistics data, with the horizontal axis representing the time axis and the vertical axis representing the start-stop frequency values ​​within the corresponding time window.

[0045] This application also provides a marine containerized battery power detection system, including a data processing module, a detection strategy generation module, and an adaptive adjustment module;

[0046] Data processing module: Automatically collects actual start-up and shutdown operation data of the ship, records the time point of each start and stop, and monitors the load change of the main power system at the moment of start-up and shutdown in real time. Analyzes the collected results and constructs a start-up and shutdown frequency statistical chart and a load change mode model, which serve as dynamic input sources.

[0047] Detection strategy generation module: After determining the current scenario based on the dynamic input source, analyze whether it is necessary to start the rapid discharge capability detection process. If so, automatically generate a battery power detection strategy based on the current scenario.

[0048] Adaptive Adjustment Module: After detecting the battery power supply through the detection strategy, it identifies whether there is a discharge voltage drop in the battery power supply. If so, it performs cumulative-variable analysis on the discharge voltage drop to determine the severity of the voltage drop in the battery power supply, and adaptively adjusts the detection process based on the judgment result.

[0049] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0050] 1. This invention constructs a start-stop frequency statistical chart and a load mutation mode model. These serve as dynamic input sources. Based on the dynamic input sources, the system determines the current scenario and analyzes whether a rapid discharge capability detection process needs to be initiated. If so, it automatically generates a battery power detection strategy based on the current scenario. After detecting the battery power using this strategy, it identifies whether there is a voltage drop in the battery power supply. If so, it performs cumulative-variable analysis on the voltage drop to determine its severity. Based on the determination result, it adaptively adjusts the detection process. This detection system can automatically identify emergency needs and quickly deploy the detection process. It dynamically adjusts the detection sampling frequency, data processing cycle, and discrimination strategy according to the severity of the voltage drop, effectively improving the system's operational efficiency, risk mitigation capabilities, dynamic responsiveness, and detection timeliness.

[0051] 2. This invention performs cumulative-variable analysis on discharge voltage drops. First, it analyzes whether the discharge voltage drop exceeds the drop threshold. If it does, it calculates the drop rate and then performs calculus on the drop rate, i.e., cumulative and fluctuation analysis. Then, it combines the cumulative and fluctuation to analyze the severity of the discharge voltage drop. This invention not only detects whether the drop exceeds the threshold, but also introduces analytical methods of derivatives and integrals such as voltage drop rate and cumulative fluctuation, comprehensively judging the severity of discharge anomalies from the time domain and trend dimensions, and constructing a more accurate fault early warning model. Attached Figure Description

[0052] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0053] Figure 1 This is a schematic diagram of the detection system of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Example 1: The method for testing the power supply of a marine containerized battery described in this example includes the following steps:

[0056] The detection system automatically collects actual start-up and shutdown operation data of the ship, records the time of each start and stop, and simultaneously monitors the load changes of the main power system at the moment of start-up and shutdown. It analyzes the collected results to construct a start-up and shutdown frequency statistical chart and a load mutation mode model. These charts and models serve as dynamic input sources. Based on the dynamic input sources, the system determines whether a rapid discharge capability detection process needs to be initiated. If so, it automatically generates a battery power detection strategy based on the current scenario. After detecting the battery power through this strategy, it identifies whether there is a discharge voltage drop. If so, it performs a cumulative-variable analysis on the discharge voltage drop (first analyzing whether the discharge voltage drop exceeds the drop threshold; if so, it calculates the drop rate and then performs calculus on the drop rate, i.e., cumulative and fluctuation analysis, and then combines the cumulative and fluctuation analyses to determine the severity of the discharge voltage drop). Based on the judgment results, the detection process is adaptively adjusted.

[0057] This application constructs a start-stop frequency statistical chart and a load mutation mode model, which serve as dynamic input sources. Based on these dynamic input sources, the system determines the current scenario and analyzes whether a rapid discharge capability detection process needs to be initiated. If so, it automatically generates a battery power supply detection strategy based on the current scenario. After detecting the battery power supply using this strategy, it identifies whether there is a voltage drop during discharge. If so, it performs cumulative-variable analysis on the voltage drop to determine its severity, and adaptively adjusts the detection process based on the assessment results. This detection system can automatically identify emergency needs and quickly deploy the detection process, dynamically adjusting the detection sampling frequency, data processing cycle, and discrimination strategy according to the severity of the voltage drop. This effectively improves the system's operational efficiency, risk mitigation capabilities, dynamic responsiveness, and detection timeliness.

[0058] This application uses cumulative-variable analysis to analyze the discharge voltage drop. First, it analyzes whether the discharge voltage drop exceeds the drop threshold. If it does, it calculates the drop rate and then performs calculus on the drop rate, i.e., cumulative and fluctuation analysis. Then, it combines the cumulative and fluctuation to analyze the severity of the discharge voltage drop. It not only detects whether the drop exceeds the threshold, but also introduces analytical methods of derivatives and integrals such as voltage drop rate and cumulative fluctuation, so as to comprehensively judge the severity of discharge anomalies from the time domain and trend dimensions, and build a more accurate fault early warning model.

[0059] Example 2: The detection system automatically collects actual start-up and stop operation data of the ship, records the time points of each start and stop, and constructs a start-up and stop frequency statistics chart, including the following steps:

[0060] The detection system activates real-time monitoring to continuously listen to control signals from the ship's main electrical system. The system collects "start" and "stop" signals from the engine control system or power management system, marking the trigger event for each start-stop operation.

[0061] The system records a precise timestamp for each start-up and stop operation, labeled as "Start Time" or "Stop Time," and includes corresponding system state transition flags (such as power on / off status, main load on / off status, etc.) to ensure that the event data has complete contextual information. All recorded start-up and stop timestamps are sorted chronologically to form a continuous sequence of start-up and stop events. This sequence can be used for subsequent time window analysis, frequency statistics, and behavior recognition.

[0062] The system sets a fixed time window (e.g., every 24 hours, every flight cycle) or a sliding time window (e.g., a rolling 6-hour interval) as the basic time unit for start-stop frequency analysis. The number of start-stop events is counted within each time window. The start-stop events within each analysis time window are statistically analyzed, and the number of starts, stops, and start-stop ratio are calculated to form basic frequency statistics.

[0063] Based on frequency statistics, a start-stop frequency chart is plotted. The horizontal axis represents time (in units of analysis time windows), and the vertical axis represents the start-stop frequency values ​​within the corresponding time window. This can be represented using a bar chart, line chart, or heatmap to visually reflect the intensity and fluctuation characteristics of start-stop activities.

[0064] Real-time monitoring of load changes in the main power system during start-up and shutdown, analysis of the collected results, and construction of a load change pattern model include the following steps:

[0065] High-speed sampling devices for electrical parameters such as voltage, current, active power, and reactive power are deployed at key load-side nodes of the main power system. The sampling frequency must meet the millisecond or sub-second level (e.g., ≥1kHz) to ensure that rapid changes during start-up and shutdown are captured. Each time a start-up or shutdown event occurs (i.e., the moment the power system starts or shuts down), the system automatically sets a load response time window (e.g., [-3s, +5s]) before and after that time point to extract load change data before and after the start-up and shutdown, forming a basis for "before and after" state comparison.

[0066] To improve the adaptability and safety of marine containerized battery power supplies under actual operating conditions, the detection system needs to possess high-precision sensing capabilities for start-up and shutdown behavior and load disturbance processes. This section of the technical solution focuses on "load change detection of the main power system at the moment of start-up and shutdown," aiming to construct a complete load change mode model through multi-parameter integrated modeling, providing dynamic input basis for subsequent rapid discharge capability testing.

[0067] The detection system automatically sets up a high-resolution sampling channel and deploys sampling probes at key nodes of the main power system, such as AC bus, main distribution board, rectifier output port or battery bus interface, to collect basic electrical parameters including voltage, current, active power and reactive power.

[0068] In actual testing, the system collects the following parameters in a time sequence:

[0069] Voltage: The unit is volt (V), which represents the instantaneous voltage value as a function of time.

[0070] Current: The unit is ampere (A), which represents the dynamic current fluctuation when the load is applied.

[0071] Active power: ;

[0072] Reactive power: ;

[0073] The continuous fluctuations of these parameters constitute the basic "signal curve" of the load's dynamic behavior. Analyzing these curves can further extract characteristics such as the amplitude, rate, and rhythm of load changes.

[0074] By recording the ship's start-stop control signals, the system automatically determines the timing of each "start" or "stop" action. Subsequently, the detection system sets a symmetrical or asymmetrical analysis time window to capture the evolution trajectory before and after load disturbances. Assume the set time window is:

[0075] ,in: For start / stop event timestamps This is a pre-analysis period (e.g., 3 seconds) before start-up and shutdown, used to assess static load; The load response period after start-up and shutdown (e.g., 5 seconds) is used to monitor the impact of disturbances. This time window not only provides a sufficient dynamic envelope, but also ensures good identification capabilities for both large sudden changes and small, stable changes.

[0076] Next, the system extracts the load change rate characteristics from the power curve. The power change rate (also known as the "transition rate") is used to reflect the intensity of the load change per unit time. The load change mode model expression is as follows: Parameter description: This indicates the rate of change of power, measured in watts per second (W / s). This is the time interval (usually set to 0.5s) used to calculate the slope. The value represents the instantaneous active power. The larger the power change rate, the greater the risk of sudden load changes in the main power system during start-up and shutdown, which may pose a challenge to the stability of battery output.

[0077] The start-stop frequency statistics chart and load change mode model serve as dynamic input sources. After determining the current scenario based on the dynamic input sources, the system analyzes whether the rapid discharge capability detection process needs to be initiated, including the following steps:

[0078] To enable the detection system to make intelligent adaptive decisions based on scenarios, the key to this step is to identify whether the current operating condition is in a "high-risk emergency state" by integrating the start-stop frequency diagram and load change model constructed from historical and real-time data, and decide whether to start the "rapid discharge capability detection process" accordingly.

[0079] The system counts the number of starts and stops per unit time period from the time series as a frequency indicator.

[0080] ,in, This refers to the start / stop frequency, measured in times per hour. For statistical time windows The total number of starts and stops recorded internally; For statistical time windows, a 1-hour period or a period based on berth / sailing cycle is typically used.

[0081] Based on the set frequency threshold Determine if it is a "high-frequency start-stop" scenario. If this value is displayed, it indicates that the battery is currently in a frequent start-stop state. This state is usually related to frequent intervention of emergency systems or sudden changes in the energy system, and battery monitoring needs to be strengthened.

[0082] After monitoring the power change rate during each start-up and shutdown using a load mutation mode model, the load mutation intensity index is calculated, and its expression is: In the formula, This is the load mutation intensity index. For statistical time windows The total number of start and stop records. Represents the rate of power change during the i-th start-stop, if ,in, If the threshold is exponential, it indicates significant load disturbance, and emergency performance testing is recommended.

[0083] Based on the system's overall start / stop frequency and load fluctuation index, the scenario levels are classified as follows:

[0084] Low-risk scenarios: If The analysis indicates a low-risk scenario, and there is no need to initiate the rapid discharge capability detection process.

[0085] Medium-risk scenario: If The scenario was analyzed as medium risk, and the rapid discharge capability detection process was initiated periodically (i.e., at pre-set time intervals).

[0086] High-risk scenarios: The analysis indicates a high-risk scenario, requiring the initiation of a rapid discharge capability testing process.

[0087] If a rapid discharge capability testing process needs to be initiated, a battery power detection strategy will be automatically generated based on the current scenario, including the following steps:

[0088] Based on the analysis results of start-stop frequency statistics and load mutation mode models, this section classifies the operating scenarios of ship power systems into multiple risk levels and formulates corresponding detection strategies for each risk level. These strategies differ significantly in terms of detection frequency, sampling granularity, discharge simulation methods, and analysis dimensions, thereby meeting the power supply assurance requirements for different load dynamic characteristics.

[0089] When a ship remains berthed for extended periods with a low frequency of starts and stops (e.g., less than twice per day), the main power system load changes slowly and without significant fluctuations, and the detection system classifies it as a low-risk scenario. In this situation, the battery power supply is primarily used to power a small number of control loops and low-power devices, the system voltage load is stable, and there is no risk of high-power discharge in the short term.

[0090] Strategy Details: The detection frequency is set to once daily or every other day; each detection process lasts approximately 5-10 minutes, simulating normal load discharge conditions; the simulated load is set to 30%-40% of the battery's rated output power; the voltage sampling frequency is maintained at 1Hz to meet the needs of tracking voltage change trends under steady state; only Boolean judgments are performed to determine whether a voltage drop has occurred, without further trend analysis; the detection results are archived for maintenance cycle trend analysis, but do not trigger active alarm mechanisms. The design goal of this strategy is to conserve resources and protect battery life, preventing the negative impact of frequent detections on battery life, while maintaining the system's basic inspection capabilities in risk-free scenarios.

[0091] When a vessel is in a phase of frequent starts and stops or frequent changes in operating conditions, such as channel changes or port operations, and the frequency of starts and stops increases significantly (more than 4 times but less than 10 times per day), while the load fluctuation has not yet exceeded the danger threshold, this is classified as a medium-risk scenario. Strategy content:

[0092] The detection frequency has been increased to 2-3 times per day to ensure coverage of different stages in the morning, noon and evening; the detection cycle remains at 5 minutes, but more complex load step simulations have been added;

[0093] The simulated load is set to 50%~70% of the rated power, covering light to medium load variations. The voltage sampling frequency is increased to 5Hz to identify more granular voltage drop trajectories. A discharge drop rate index is introduced to assess the voltage drop trend over a short period of time. If the rate exceeds the threshold during detection, an early warning is triggered and the system is marked as a potential risk record. This strategy improves the accuracy of identifying sub-optimal power supplies while maintaining detection efficiency, and proactively detects potential performance degradation through trend analysis of voltage changes.

[0094] When the start-stop frequency approaches its limit (e.g., more than once per hour) and there are drastic load changes (e.g., load fluctuations exceeding 70% of the system's maximum capacity), the system will determine that it is currently in a high-risk scenario. Typical applications include emergency start-up, changes in wind and wave conditions, and abnormal switching of power subsystems.

[0095] Strategy Details: A high-frequency detection mechanism is activated, with a detection frequency up to once every 2 hours. The simulated load is set to 80%~100% of the rated output to simulate actual sudden discharge demands. The sampling frequency is increased to 10Hz or even higher to accurately capture voltage fluctuations at the second level. Multivariate analysis is incorporated into the detection process, such as instantaneous voltage drop value, drop rate, and fluctuation integral. Once the system detects that an indicator exceeds the limit, a fault warning will be triggered, and it will be recommended to immediately switch to backup power or perform emergency maintenance. This strategy represents the strongest real-time dynamic response capability, sacrificing some system resources for the highest level of safety assurance for battery discharge performance.

[0096] By establishing a risk-level-based detection strategy selection mechanism, the marine containerized battery power supply detection system achieves an adaptive detection process strongly coupled with the operating scenario. This solution effectively overcomes the shortcomings of traditional detection systems, such as single cycle, coarse detection granularity, and delayed response, enabling the system to achieve "early warning, dynamic tracking, and intelligent control" in critical operating conditions. The strategy grading mechanism also significantly improves battery life management capabilities, avoiding frequent detections in unnecessary scenarios and reducing the occupation of system resources and battery wear and tear.

[0097] After detecting the battery power supply using a detection strategy, the system identifies whether there is a voltage drop in the battery power supply, including the following steps:

[0098] After performing discharge detection on the battery power supply using the detection strategy, the system needs to conduct in-depth analysis of the voltage data collected during the discharge process to identify whether there is a discharge voltage drop. Discharge voltage drop is an important indicator for measuring the battery's performance stability and responsiveness under sudden load changes.

[0099] During the execution of the detection strategy, the system continuously collects voltage data at the battery output terminal during the discharge phase according to a preset sampling frequency (e.g., 1Hz for low risk, 5Hz for medium risk, and 10Hz for high risk). The collected data is recorded as follows: ,in, Indicates the first Each sampling time point This is the total number of samples taken within the testing period, combined with the battery's nominal voltage. Minimum operating voltage In addition to system load, define the discharge voltage drop threshold. The value is usually: ,in This is the sensitivity coefficient; the higher the coefficient, the more stringent the detection requirement. Throughout the entire detection cycle, the system iterates through all voltage data to determine if there is a voltage value at any arbitrary time point. satisfy: If the condition is met, it is marked as a voltage drop; otherwise, it is judged as normal discharge.

[0100] If a discharge voltage drop occurs, a cumulative-variable analysis is performed on the discharge voltage drop (first analyze whether the discharge voltage drop exceeds the drop threshold; if it does, calculate the drop rate, then perform calculus on the drop rate, i.e., cumulative and fluctuation analysis, and then combine the cumulative and fluctuation analysis to determine the severity of the discharge voltage drop), to determine the severity of the battery power supply voltage drop, and adaptively adjust the detection process based on the determination results, including the following steps:

[0101] like To determine if a discharge voltage drop exists and calculate the drop rate, the expression is:

[0102]

[0103] in, For the rate of fall, The voltage at which the voltage drops to the starting point. The voltage at the lowest voltage point. As the starting point of the fall, The lowest voltage point is the lowest voltage point. This rate represents the magnitude of voltage drop per unit time and is an important parameter reflecting the rate at which the battery's power supply capacity degrades with sudden load changes.

[0104] To analyze the "trend" of the entire voltage change process, the system integrates the rate of voltage drop during the drop process to obtain the total voltage drop, which represents the magnitude of the total voltage loss. The expression is as follows:

[0105] In the formula, This represents the total voltage loss amplitude. For the detection duration, The drop rate is denoted by t. The larger the total voltage drop amplitude, the more energy is lost during the entire drop process, and the weaker the battery's short-term support capability.

[0106] Further analysis of the fluctuations in the voltage sag curve is needed, specifically examining whether the voltage response exhibits severe oscillations or instability. The amplitude of the voltage sag fluctuation is calculated using the following expression: In the formula, The fluctuation range is the value of the drop. For the number of monitoring points, Let be the fall rate at the i-th monitoring point. The drop rate is the average. The smaller the drop rate fluctuation amplitude, the smaller the drop rate fluctuation within the detection period.

[0107] If the total voltage drop amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is less than or equal to the fluctuation threshold, the analysis indicates that the voltage drop is severe within the detection period, and the sampling frequency needs to be increased by 10% based on the current scenario.

[0108] If the total voltage drop amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is greater than the fluctuation threshold, the analysis indicates that the voltage drop is moderate within the detection time (there may be some monitoring points where the voltage drop rate is less than the loss threshold). Therefore, the sampling frequency needs to be increased by 5% based on the current scenario.

[0109] If the total voltage drop amplitude is less than the loss threshold, the analysis shows that the voltage drop is good within the detection period and no adjustment is required.

[0110] Example 3: In this example, a scoring function can be obtained by weighting the start-stop frequency and the load mutation index, and then the scenario can be divided according to the scoring function. The specific scheme is as follows:

[0111] To achieve intelligent adaptive functionality in the marine containerized battery power supply detection system, the system first calculates the risk level based on two core indicators: start-stop frequency and load surge intensity index. Start-stop frequency This refers to the number of times a ship's electrical system starts and stops within a unit of time, usually counted hourly or daily. Load Sudden Change Intensity Index Reflecting the degree of change in current retainability during instantaneous start-up and shutdown of the main power system, the two indicators, after standardization, are mapped to a unified risk scoring function, the expression of which is: ,in, To score the risk, , As a weighting factor, and In this application, , The frequency and mutation rates are determined by their weighting on the overall risk level. This is the maximum start / stop frequency. This is the maximum load mutation intensity index. Based on... As a result, the system divides the scenarios into three levels: low-risk scenarios, medium-risk scenarios, and high-risk scenarios, each corresponding to a different detection strategy.

[0112] The obtained risk score is compared with the preset first and second scoring thresholds. If the first scoring threshold is less than the second scoring threshold, the current scenario is classified as a low-risk scenario. If the risk score is greater than the first scoring threshold and less than or equal to the second scoring threshold, the current scenario is classified as a medium-risk scenario. If the risk score is greater than the second scoring threshold, the current scenario is classified as a high-risk scenario.

[0113] Using a weighted calculation method for scene segmentation allows for a more comprehensive and accurate approach.

[0114] Example 4: Please refer to Figure 1 As shown, the marine containerized battery power detection system described in this embodiment includes a data processing module, a detection strategy generation module, and an adaptive adjustment module.

[0115] Data processing module: Automatically collects actual start-up and stop operation data of the ship, records the time point of each start and stop, and monitors the load change of the main power system at the moment of start-up and stop in real time. Analyzes the collected results, constructs a start-up and stop frequency statistical chart and a load change mode model. The start-up and stop frequency statistical chart and the load change mode model serve as dynamic input sources, which are sent to the detection strategy generation module.

[0116] Detection strategy generation module: After determining the current scenario based on the dynamic input source, it analyzes whether the rapid discharge capability detection process needs to be started. If so, it automatically generates a battery power detection strategy based on the current scenario and sends the battery power detection strategy to the adaptive adjustment module.

[0117] Adaptive Adjustment Module: After detecting the battery power supply through the detection strategy, it identifies whether there is a discharge voltage drop. If so, it performs a cumulative-variable analysis on the discharge voltage drop (first analyzes whether the discharge voltage drop exceeds the drop threshold; if it does, it calculates the drop rate and then performs calculus on the drop rate, i.e., cumulative and fluctuation analysis, and then combines the cumulative and fluctuation to analyze the severity of the discharge voltage drop). It determines the severity of the battery power supply voltage drop and adaptively adjusts the detection process based on the judgment result. The adjusted detection process is then fed back to the detection strategy generation module.

[0118] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0119] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0121] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method of detecting a battery power source for a marine container, characterized by: The detection method includes the following steps: After the detection system constructs a start-stop frequency statistical chart and a load change mode model, it determines the current scenario and analyzes whether the discharge capability detection process needs to be started based on the current scenario determination results. If necessary, a battery power detection strategy can be automatically generated based on the current scenario, and after detecting the battery power through the detection strategy, it can be identified whether there is a drop in the discharge voltage of the battery power. If present, a cumulative-variable analysis is performed on the discharge voltage drop to determine the severity of the voltage drop in the battery power supply. Based on the determination result, the detection process is adaptively adjusted. The cumulative-variable analysis first analyzes whether the discharge voltage drop exceeds the drop threshold. If it does, the drop rate is calculated, and then a calculus operation is performed on the drop rate, i.e., the cumulative amount and fluctuation amount are analyzed. Then, the severity of the discharge voltage drop is analyzed by combining the cumulative amount and fluctuation amount.

2. The method of claim 1, wherein: To determine the severity of the voltage drop in the battery power supply, a cumulative-variable analysis is performed on the discharge voltage drop, including the following steps: like To determine if a discharge voltage drop exists and calculate the drop rate, the expression is: ; wherein, is a drop rate, is a drop start voltage, is a minimum voltage point voltage, is a drop start, is a minimum voltage point, the rate indicating the magnitude of voltage drop per unit time; The total voltage loss amplitude is obtained by integrating the drop rate during the drop process, and the expression is: , wherein, is the total voltage loss amplitude, is the detection duration, is the drop rate at time t. The drop fluctuation amplitude is calculated, and the expression is: , wherein, is the drop fluctuation amplitude, is the number of monitoring points, is the drop rate at the i-th monitoring point, is the average drop rate; If the total voltage drop amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is less than or equal to the fluctuation threshold, the analysis indicates that the voltage drop is severe within the detection period. If the total voltage loss amplitude is greater than or equal to the loss threshold, and the voltage drop fluctuation amplitude is greater than the fluctuation threshold, the analysis indicates that the voltage drop is moderate within the detection time. If the total voltage loss amplitude is less than the loss threshold, the analysis indicates that the voltage drop is good within the detection period.

3. The method of claim 2, wherein the method is used for a marine container-type battery power supply. Identifying whether there is a voltage drop in the battery power supply includes the following steps: During the execution of the detection strategy, voltage data at the battery output terminal during the discharge phase is continuously collected at a preset sampling frequency. The collected data is recorded as follows: ,in, Indicates the first Each sampling time point To determine the total number of samples within the detection period, iterate through all voltage data and determine if there exists a voltage value at any point in time. satisfy: If the condition is met, it is marked as a voltage drop; otherwise, it is judged as normal discharge. This is the discharge voltage drop threshold.

4. The method for testing the power supply of a marine containerized battery according to claim 1, characterized in that: Determine the current scenario and, based on the scenario determination result, analyze whether the discharge capability detection process needs to be initiated, including the following steps: Risk level is calculated based on start / stop frequency and load mutation intensity index. Start / stop frequency This refers to the number of times a ship's electrical system starts and stops per unit time, and the load mutation intensity index. It reflects the degree of change in current retention rate during instantaneous start-up and shutdown of the main power system. After standardizing the start-up and shutdown frequency and load change intensity index, the risk score is calculated through the risk scoring function. The obtained risk score is compared with a preset first scoring threshold and a second scoring threshold, wherein the first scoring threshold is less than the second scoring threshold; If the risk score is less than or equal to the first scoring threshold, the current scenario is classified as a low-risk scenario. If the risk score is greater than the first scoring threshold and less than or equal to the second scoring threshold, the current scenario is classified as a medium-risk scenario. If the risk score is greater than the second scoring threshold, the current scenario is classified as a high-risk scenario.

5. The method for testing the power supply of a marine containerized battery according to claim 1, characterized in that: Determining the current scenario and analyzing whether the discharge capability detection process needs to be initiated based on the scenario determination results also includes the following steps: The start-stop frequency is obtained by counting the number of starts and stops per unit time in the time series. After monitoring the power change rate during each start-up and shutdown using a load mutation mode model, the load mutation intensity index is calculated. Based on start / stop frequency and load mutation index, the scenario levels are divided as follows: Low-risk scenarios: If The rapid discharge capability testing process is not initiated. Medium-risk scenario: If Regularly initiate the rapid discharge capability testing process; High-risk scenarios: Initiate the rapid discharge capability testing process; in, For start / stop frequency, This is the load mutation intensity index. For frequency threshold, The threshold value is used as the exponential threshold. After monitoring the power change rate during each start-up and shutdown using the load mutation mode model, the load mutation intensity index is calculated, and its expression is: In the formula, This is the load mutation intensity index. For statistical time windows The total number of start and stop records. This represents the rate of power change during the i-th start-stop.

6. The method for testing the power supply of a marine containerized battery according to claim 5, characterized in that: The start / stop frequency is obtained by counting the number of starts and stops per unit time in a time series. The expression is: ,in, For start / stop frequency, For statistical time windows The total number of starts and stops recorded internally; This represents the statistical time window.

7. The method for testing the power supply of a marine containerized battery according to claim 5, characterized in that: Constructing a load mutation pattern model includes the following steps: The detection system collects voltage, current, active power, and reactive power data from the main power system. By recording the ship's start-stop control signals, the system automatically determines the timing of each start-up or stop action and sets an analysis time window to capture the evolution trajectory before and after load disturbances. The time window is as follows: ,in: For start / stop event timestamps This is the pre-analysis period before start-up and shutdown. The load response period after start-up and shutdown; The rate characteristics of load change are extracted from the power curve to reflect the intensity of load abrupt changes per unit time, generating a load abrupt change mode model, expressed as: ,in: Indicates the rate of change of power. For time intervals, This refers to the instantaneous active power.

8. The method for testing the power supply of a marine containerized battery according to claim 7, characterized in that: Constructing a start / stop frequency statistics chart includes the following steps: Record a timestamp for each start-up and stop operation, labeled as start time or stop time, and include the corresponding status switching flags, including power on / off status and main load on / off status. The number of start-stop events is counted within a fixed or sliding time window. The start-stop events within each time window are statistically analyzed, and the number of start-stop events, the number of stop-start events, and the start-stop ratio within that time window are calculated to form frequency statistics. A start-stop frequency statistics chart is drawn based on frequency statistics data, with the horizontal axis representing the time axis and the vertical axis representing the start-stop frequency values ​​within the corresponding time window.

9. A marine containerized battery power testing system, used to implement the testing method according to any one of claims 1-8, characterized in that: It includes a data processing module, a detection strategy generation module, and an adaptive adjustment module; Data processing module: Automatically collects actual start-up and shutdown operation data of the ship, records the time point of each start and stop, and monitors the load change of the main power system at the moment of start-up and shutdown in real time. Analyzes the collected results and constructs a start-up and shutdown frequency statistical chart and a load change mode model, which serve as dynamic input sources. Detection strategy generation module: After determining the current scenario based on the dynamic input source, analyze whether it is necessary to start the rapid discharge capability detection process. If so, automatically generate a battery power detection strategy based on the current scenario. Adaptive Adjustment Module: After detecting the battery power supply through the detection strategy, it identifies whether there is a discharge voltage drop. If so, it performs cumulative-variable analysis on the discharge voltage drop to determine the severity of the voltage drop. Based on the determination result, it adaptively adjusts the detection process. The cumulative-variable analysis first analyzes whether the discharge voltage drop exceeds the drop threshold. If it does, it calculates the drop rate and then performs calculus on the drop rate, i.e., cumulative amount and fluctuation amount analysis. Finally, it combines the cumulative amount and fluctuation amount to analyze the severity of the discharge voltage drop.

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