Marine container type storage battery power supply detection system and method

Abstract

The invention discloses a container type storage battery power supply detection system and method for a ship, and relates to the technical field of power supply detection systems.A start-stop frequency statistical graph and a load sudden change mode model are constructed, the start-stop frequency statistical graph and the load sudden change mode model are used as dynamic input sources, and after a current scene is judged according to the dynamic input sources, the storage battery power supply is detected. Analyzing whether a rapid discharge capability detection process needs to be started, if so, automatically generating a storage battery power supply detection strategy based on the current scene, detecting the storage battery power supply through the detection strategy, then identifying whether the discharge voltage drop exists in the battery power supply, and if so, carrying out cumulative-variable analysis on the discharge voltage drop; and judging the voltage drop severity of the battery power supply, and carrying out self-adaptive adjustment on the detection process according to a judgment result. According to the detection system, the detection sampling frequency, the data processing period and the judgment strategy are dynamically adjusted according to the drop severity, and the operation and maintenance efficiency, the risk suppression capability, the dynamic responsiveness and the detection timeliness of the detection system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power detection systems, and particularly to a marine containerized battery power detection system and method. Background Art

[0002] In modern ship power systems, batteries are not only used for starting the main engine, electric propulsion, and emergency power supply, but also widely participate in the stable operation and safety guarantee of the power system. With the rise of new energy ships, pure electric ships, and hybrid power ships, the battery capacity is continuously increasing, and the system structure is becoming more complex, which puts forward higher requirements for the operation safety, performance monitoring, and life assessment of batteries.

[0003] The existing technologies have the following deficiencies:

[0004] 1. Most of the existing systems judge the detection timing based on a fixed cycle or static voltage, without considering the action mechanism of actual load disturbances on voltage response, resulting in that the detection often cannot be triggered at the most critical load impact moment, and there are problems of poor detection timeliness and delayed performance evaluation;

[0005] 2. The detection process is a fixed-step process and cannot automatically adjust the detection strategy according to the actual ship operation status (such as start-stop frequency change, external temperature, battery aging status), resulting in either resource waste in detection or missing the critical hidden danger identification window due to response lag.

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

[0007] The purpose of the present invention is to provide a marine containerized battery power detection system and method to solve the deficiencies in the background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A marine containerized battery power detection method, the detection method includes the following steps:

[0009] After the detection system constructs a start-stop frequency statistical chart and a load mutation mode model, it determines the current scenario, and analyzes whether it is necessary to start the discharge capacity detection process based on the determination result of the current scenario;

[0010] If necessary, automatically generate a battery power detection strategy based on the current scenario, and after detecting the battery power through the detection strategy, identify whether there is a discharge voltage drop in the battery power;

[0011] If it exists, perform cumulative-variation analysis on the discharge voltage drop to determine the severity of the voltage drop of the battery power supply, and adaptively adjust the detection process according to the judgment result.

[0012] In a preferred embodiment, performing cumulative-variation analysis on the discharge voltage drop to determine the severity of the voltage drop of the battery power supply includes the following steps:

[0013] If V(t i ) < V drop_th , it is determined that there is a discharge voltage drop, and the drop rate is calculated. The expression is:

[0014] Integrate the drop rate during the drop process to obtain the total voltage loss amplitude. The expression is: In the formula, S drop is the total voltage loss amplitude, T is the detection duration, and r drop (t) is the drop rate at time t;

[0015] Calculate the drop fluctuation amplitude. The expression is: In the formula, W vol is the drop fluctuation amplitude, M is the number of monitoring points, is the drop rate at the i-th monitoring point, is the average drop rate;

[0016] If the total voltage loss amplitude is greater than or equal to the loss threshold and the drop fluctuation amplitude is less than or equal to the fluctuation threshold, it is analyzed that the voltage drop is severe within the detection duration;

[0017] If the total voltage loss amplitude is greater than or equal to the loss threshold and the drop fluctuation amplitude is greater than the fluctuation threshold, it is analyzed that the voltage drop is medium within the detection duration;

[0018] If the total voltage loss amplitude is less than the loss threshold, it is analyzed that the voltage drop is good within the detection duration.

[0019] In a preferred embodiment, identifying whether there is a discharge voltage drop in the battery power supply includes the following steps:

[0020] During the execution of the detection strategy, continuously collect the voltage data at the output end of the battery during the discharge stage according to the preset sampling frequency. The collected data is recorded as: V(t i ), i = 1, 2,..., N, where t i represents the i-th sampling time point, N is the total number of samplings within the detection period. Traverse all the voltage data to determine whether there is a voltage value V(t i ) that satisfies: V(t i ) < V drop_th . If it is satisfied, it is marked as a voltage drop, otherwise it is judged as normal discharge. Among them, V drop_this the discharge voltage drop threshold.

[0021] In a preferred embodiment, the current scenario is determined, and based on the determination result of the current scenario, it is analyzed whether it is necessary to start the discharge capacity detection process, including the following steps:

[0022] Calculate the risk level according to the start-stop frequency and the load mutation intensity index. The start-stop frequency F startstop refers to the number of starts and stops of the ship's power system per unit time. The load mutation intensity index L jump reflects the degree of mutation of the current still rate in the main power system during the instantaneous start-stop process. After normalizing the start-stop frequency and the load mutation intensity index, the risk score is calculated through the risk scoring function;

[0023] Compare the obtained risk score with a preset first score threshold and a second score threshold, and the first score threshold is less than the second score threshold;

[0024] If the risk score is less than or equal to the first score threshold, the current scenario is classified as a low-risk scenario. If the risk score is greater than the first score threshold and less than or equal to the second score threshold, the current scenario is classified as a medium-risk scenario. If the risk score is greater than the second score threshold, the current scenario is classified as a high-risk scenario.

[0025] In a preferred embodiment, the current scenario is determined, and based on the determination result of the current scenario, it is also analyzed whether it is necessary to start the discharge capacity detection process, including the following steps:

[0026] Count the number of starts and stops per unit time from the time series to obtain the start-stop frequency;

[0027] After monitoring the power change rate during each start and stop through the load mutation mode model, calculate the load mutation intensity index;

[0028] Combining the start-stop frequency and the load mutation index, the scenario level is divided into:

[0029] Low-risk scenario: If F startstop <F th ∧L jump <L th , do not start the fast discharge capacity detection process;

[0030] Medium-risk scenario: If F startstop ≥F th ∧L jump <L th , regularly start the fast discharge capacity detection process;

[0031] High-risk scenario: F startstop ≥F th ∧L jump ≥Lth , start the fast discharge capacity detection process;

[0032] Among them, F startstop is the start-stop frequency, L jump is the load mutation intensity index, F th is the frequency threshold, L th is the index threshold.

[0033] In a preferred embodiment, the number of start-stops per unit time is statistically obtained from the time series to obtain the start-stop frequency, and the expression is: Among them, F startstop is the start-stop frequency, N startstop is the total number of start-stops recorded within the statistical time window T window ; T window is the statistical time window.

[0034] In a preferred embodiment, after monitoring the power change rate at each start-stop through the load mutation mode model, the load mutation intensity index is calculated, and the expression is: In the formula, L jump is the load mutation intensity index, N startstop is the total number of start-stops recorded within the statistical time window T window , ΔP rate represents the power change rate at the i-th start-stop.

[0035] In a preferred embodiment, constructing a load mutation mode model includes the following steps:

[0036] The detection system collects the voltage, current, active power, and reactive power of the main power system;

[0037] By recording the start-stop control signal of the ship, automatically determine the time point of each start or stop action, and set the analysis time window to capture the evolution trajectory before and after the load disturbance. The time window is: [t0 - T1, t0 + T2], where: t0 is the time stamp of the start-stop event, T1 is the pre-analysis period before the start-stop, and T2 is the load response period after the start-stop;

[0038] Extract the rate characteristics of the load change from the power curve to reflect the mutation intensity of the load per unit time, and generate a load mutation mode model. The expression is:

[0039] Among them: ΔP rate represents the power change rate, δ is the time interval, and P(t) is the instantaneous active power.

[0040] In a preferred embodiment, constructing a start-stop frequency statistical chart includes the following steps:

[0041] Record the timestamp for each start-stop operation, label it as the start time or stop time, and attach the corresponding status change flag, including the power on / off status and the main load on / off status;

[0042] Count the start-stop times within a set fixed time window or sliding time window, count the start-stop events within each time window, calculate the number of starts, stops, and start-stop ratio within that time window, and form frequency statistics data;

[0043] Draw a start-stop frequency statistical graph based on the frequency statistics data, with the horizontal axis as the time axis and the vertical axis as the start-stop frequency value within the corresponding time window.

[0044] 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;

[0045] Data processing module: Automatically collect the actual start-stop operation data of the ship, record the time points of each start and stop, and at the same time, monitor the load change of the main power system at the moment of start-stop in real time, analyze the acquisition results, construct a start-stop frequency statistical graph and a load mutation mode model, and use the start-stop frequency statistical graph and the load mutation mode model as dynamic input sources;

[0046] Detection strategy generation module: After determining the current scenario based on the dynamic input source, analyze whether it is necessary to start the fast discharge capacity detection process. If so, automatically generate a battery power detection strategy based on the current scenario;

[0047] Adaptive adjustment module: After detecting the battery power through the detection strategy, identify whether there is a discharge voltage drop in the battery power. If so, perform cumulative-variable analysis on the discharge voltage drop, judge the severity of the voltage drop of the battery power, and adaptively adjust the detection process according to the judgment result.

[0048] In the above technical solution, the technical effects and advantages provided by the present invention:

[0049] 1. The present invention constructs a start-stop frequency statistical chart and a load mutation mode model. The start-stop frequency statistical chart and the load mutation mode model serve as dynamic input sources. After determining the current scenario based on the dynamic input sources, it analyzes whether it is necessary to start the fast discharge capacity detection process. If so, it automatically generates a battery power detection strategy based on the current scenario. After detecting the battery power through the detection strategy, it identifies whether there is a discharge voltage drop in the battery power. If there is, it performs a cumulative-variation analysis on the discharge voltage drop to determine the severity of the voltage drop of the battery power, and adaptively adjusts the detection process according to the judgment result. This detection system can automatically identify emergency requirements and quickly deploy the detection process, dynamically adjust the detection sampling frequency, data processing period, and discrimination strategy according to the severity of the drop, effectively improving the operation and maintenance efficiency, risk suppression ability, dynamic responsiveness, and detection timeliness of the detection system.

[0050] 2. The present invention performs a cumulative-variation analysis on the discharge voltage drop. First, it analyzes whether the discharge voltage drop exceeds the drop threshold. If it exceeds, after calculating the drop rate, it performs a calculus operation on the drop rate, that is, cumulative amount and fluctuation amount analysis. Then, it combines the cumulative amount and the fluctuation amount to analyze the severity of the discharge voltage drop, not only detecting whether the drop exceeds the threshold, but also introducing analysis means such as the voltage drop rate and cumulative fluctuation amount, which are derivatives and integral magnitudes, to comprehensively judge the severity of the discharge anomaly from the time domain and trend dimensions, and constructs a more accurate fault warning model. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0052] Figure 1 It is a framework diagram of the detection system of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the protection scope of the present invention.

[0054] Embodiment 1: The shipborne container-type battery power detection method described in this embodiment includes the following steps:

[0055] The detection system automatically collects the actual start-stop operation data of the ship, records the time points of each start and stop, and simultaneously monitors the load changes of the main power system at the moment of start and stop in real time. After analyzing the acquisition results, it constructs a start-stop frequency statistical chart and a load mutation mode model. The start-stop frequency statistical chart and the load mutation mode model serve as dynamic input sources. After determining the current scenario based on the dynamic input sources, it analyzes whether it is necessary to start the fast discharge capacity detection process. If so, it automatically generates a battery power supply detection strategy based on the current scenario. 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 there is, it conducts a cumulative-variation analysis of the discharge voltage drop (first analyze whether the discharge voltage drop exceeds the drop threshold. If it exceeds, calculate the drop rate and then perform a calculus operation on the drop rate, that is, cumulative amount and fluctuation amount analysis. Then, combine the cumulative amount and the fluctuation amount to analyze the severity of the discharge voltage drop), judge the severity of the voltage drop of the battery power supply, and adaptively adjust the detection process according to the judgment result.

[0056] In this application, by constructing a start-stop frequency statistical chart and a load mutation mode model, the start-stop frequency statistical chart and the load mutation mode model serve as dynamic input sources. After determining the current scenario based on the dynamic input sources, it analyzes whether it is necessary to start the fast discharge capacity detection process. If so, it automatically generates a battery power supply detection strategy based on the current scenario. 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 there is, it conducts a cumulative-variation analysis of the discharge voltage drop, judges the severity of the voltage drop of the battery power supply, and adaptively adjusts the detection process according to the judgment result. This detection system can automatically identify emergency requirements and quickly deploy the detection process, dynamically adjust the detection sampling frequency, data processing period and discrimination strategy according to the severity of the drop, effectively improving the operation and maintenance efficiency, risk suppression ability, dynamic responsiveness and detection timeliness of the detection system.

[0057] In this application, by conducting a cumulative-variation analysis of the discharge voltage drop, first analyze whether the discharge voltage drop exceeds the drop threshold. If it exceeds, calculate the drop rate and then perform a calculus operation on the drop rate, that is, cumulative amount and fluctuation amount analysis. Then, combine the cumulative amount and the fluctuation amount to analyze the severity of the discharge voltage drop. It not only detects whether the drop exceeds the threshold, but also introduces derivative and integral level analysis means such as voltage drop rate and cumulative fluctuation amount, comprehensively judges the severity of discharge abnormality from the time domain and trend dimensions, and constructs a more accurate fault warning model.

[0058] Embodiment 2: The detection system automatically collects the actual start-stop operation data of the ship, records the time points of each start and stop, and constructs a start-stop frequency statistical chart, including the following steps:

[0059] The detection system enables the real-time monitoring function and continuously listens to the control signals of the ship's main power system. The system marks the trigger events of each start-stop operation by collecting the "start" and "stop" signals in the engine control system or the power management system.

[0060] The system records accurate timestamps for each start-stop operation, marked as "start time" or "stop time", and also attaches the corresponding system state change flags (such as power on / off state, main load on / off state, etc.) to ensure that the event data has complete context information. All the recorded start-stop timestamps are sorted in chronological order to form a continuous sequence of start-stop events. This sequence can be used for subsequent time window analysis, frequency statistics, and behavior recognition.

[0061] The system sets fixed time windows (such as every 24 hours, each voyage cycle) or sliding time windows (such as a rolling 6-hour interval) as the basic time units for start-stop frequency analysis. The number of start-stop operations is counted within each time window. The start-stop events within each analysis time window are statistically analyzed to calculate the number of starts, stops, and the start-stop ratio during that period, forming the basic frequency statistics data.

[0062] Based on the frequency statistics data, a start-stop frequency statistical chart is drawn. The horizontal axis is the time axis (in units of the analysis time window), and the vertical axis is the start-stop frequency value within the corresponding time window. It can be represented in the form of a bar chart, line chart, or heat map to visually reflect the density and fluctuation characteristics of the start-stop activities.

[0063] The detection system monitors the load change situation of the main power system at the moment of start and stop in real time, analyzes the acquisition results, and constructs a load mutation mode model, including the following steps:

[0064] High-speed sampling devices for electrical parameters such as voltage, current, active power, and reactive power are deployed at the key load-side nodes of the main power system. The sampling frequency needs to meet the millisecond or sub-second level (such as ≥1kHz) to ensure capturing the rapid changes at the moment of start and stop. Each time a start-stop event occurs (i.e., the moment when the power system starts or disconnects), the system automatically sets a load response time window (such as [-3s, +5s]) before and after this time point to extract the load change data before and after start and stop, forming a basis for "before and after state" comparison.

[0065] To improve the adaptability and safety of marine containerized battery power sources under actual working conditions, the detection system needs to have a high-precision perception ability for start-stop behaviors and load disturbance processes. This part of the technical solution focuses on "detecting the load change of the main power system at the moment of start and stop", aiming to construct a complete load mutation mode model through multi-parameter comprehensive modeling to provide a dynamic input basis for subsequent rapid discharge capacity detection.

[0066] The detection system will automatically set up high-resolution sampling channels and deploy sampling probes at key nodes of the main power system, such as the AC bus, main switchboard, rectifier output port, or battery bus interface, etc., to collect basic electrical parameters including voltage, current, active power, and reactive power.

[0067] In actual detection, the system collects the following parameters in time sequence:

[0068] Voltage: U(t), in volts (V), representing the instantaneous voltage value varying with time;

[0069] Current: I(t), in amperes (A), representing the dynamic current fluctuation when the load is pulled;

[0070] Active power: P(t);

[0071] Reactive power: Q(t);

[0072] The continuous fluctuations of these parameters constitute the basic "signal curve" of the load dynamic behavior. By analyzing it, characteristics such as the amplitude, rate, and rhythm of the load change can be further extracted.

[0073] By recording the start-stop control signals of the ship, the time points of each "start" or "stop" action are automatically determined. Subsequently, the detection system sets a symmetric or asymmetric analysis time window to capture the evolution trajectory before and after the load disturbance. Suppose the set time window is:

[0074] [t0 - T1, t0 + T2], where: t0 is the time stamp of the start-stop event, T1 is the pre-analysis period before start-stop (such as 3 seconds) for evaluating the static load; T2 is the load response period after start-stop (such as 5 seconds) for monitoring the disturbance impact. This time window not only provides an adequate dynamic envelope but also ensures good discrimination ability for both large sudden changes and small smooth changes.

[0075] Next, the system extracts the rate characteristic of the load change from the power curve. The power change rate (also known as the "transition rate") is used to reflect the mutation intensity of the load per unit time. The load mutation mode model expression is: Parameter description: ΔP rate Represents the power change rate, in watts per second (W / s), δ is the time interval (usually set to 0.5 s) for calculating the slope, P(t) is the instantaneous active power. The larger the power change rate value, the greater the risk of load mutation at the start-stop moment of the main power system, and the more likely it is to pose a challenge to the output stability of the battery.

[0076] The start-stop frequency statistical chart and the load mutation mode model are used as dynamic input sources. After determining the current scenario based on the dynamic input sources, analyze whether it is necessary to start the fast discharge capacity detection process, including the following steps:

[0077] To enable the detection system to have the decision-making ability of intelligent adaption based on scenarios, the key to this step is: by fusing the start-stop frequency chart and the load mutation model constructed from historical and real-time data, identify whether the current operating condition is in a "high-risk emergency state", and accordingly decide whether to start the "fast discharge capacity detection process".

[0078] The system counts the number of start-stops per unit time from the time series as the frequency index.

[0079] Among them, F startstop is the start-stop frequency, with the unit of times per hour; N startstop is the total number of start-stops recorded within the statistical time window T window ; T window is the statistical time window, usually taking 1 hour or being divided by berth / sailing cycle.

[0080] According to the set frequency threshold F th judge whether it is a "high-frequency start-stop" scenario. If F startstop ≥F th , then mark the current as being in a frequent start-stop state. This state is usually related to the frequent intervention of the emergency system and the mutation of the energy system, and the battery detection needs to be strengthened.

[0081] After monitoring the power change rate during each start-stop through the load mutation mode model, calculate the load mutation intensity index, and the expression is: In the formula, L jump is the load mutation intensity index, N startstop is the total number of start-stops recorded within the statistical time window T window , ΔP rate represents the power change rate at the i-th start-stop. If L jump ≥L th , among which, L th is the index threshold, it indicates that the load disturbance is significant, and it is recommended to conduct emergency performance detection.

[0082] The system comprehensively combines the start-stop frequency and the load mutation index to classify the scenario levels as follows:

[0083] Low-risk scenario: If F startstop <F th ∧L jump <L th , it is analyzed as a low-risk scenario, and there is no need to start the fast discharge capacity detection process;

[0084] Medium-risk scenario: If F startstop ≥F th ∧L jump <L th , it is analyzed as a medium-risk scenario, and the fast discharge capacity detection process is started regularly (i.e., started at a pre-set time interval);

[0085] High-risk scenario: F startstop ≥F th ∧L jump ≥L th , it is analyzed as a high-risk scenario, and the fast discharge capacity detection process needs to be started.

[0086] If the fast discharge capacity detection process needs to be started, a battery power supply detection strategy is automatically generated based on the current scenario, including the following steps:

[0087] This part divides the operation scenarios of the ship's power system into multiple risk levels based on the analysis results of the start-stop frequency statistical chart and the load mutation mode model, and formulates corresponding detection strategies for each risk level. There are significant differences in detection frequency, sampling granularity, discharge simulation method, analysis dimension, etc. among various strategies, so as to meet the power supply guarantee requirements of different load dynamic characteristics.

[0088] When the ship is in the berth state for a long time, the start-stop frequency is at a low level (such as less than 2 times a day), the load of the main power system changes slowly and there is no obvious fluctuation, and the detection system determines it as a low-risk scenario. In this situation, the battery power supply is mostly used to supply power to a small number of control circuits and low-power devices, the system voltage load is stable, and there is no risk of high-power discharge in the short term.

[0089] Strategy content: The detection frequency is set to once a day or once every other day; each detection process lasts about 5-10 minutes, simulating the discharge of a conventional load; the simulated load is set to 30%-40% of the rated output power of the battery; the voltage sampling frequency is kept at 1 Hz to meet the tracking of the voltage change trend under steady state; only a Boolean determination is made on whether a voltage dip occurs, and no further trend analysis is carried out; the detection results are archived for maintenance cycle trend analysis, but no active alarm mechanism is triggered. The design goal of this strategy is resource conservation and life protection, preventing the negative impact of frequent detection on battery life, and maintaining the basic inspection ability of the system in a risk-free scenario.

[0090] When the ship is in a stage of frequent start-stop or frequent working condition switching, such as channel switching, port operation, etc., the start-stop frequency increases significantly (more than 4 times but less than 10 times a day), and the load mutation has not exceeded the dangerous threshold, and it is determined as a medium-risk scenario at this time. Strategy content:

[0091] The detection frequency is increased to 2 - 3 times a day to ensure coverage of different stages in the morning, noon, and evening; the detection period remains 5 minutes, but more complex load step simulations are added;

[0092] The simulated load is set to 50% - 70% of the rated power to cover the change from light load to medium load. The voltage sampling frequency is increased to 5 Hz to identify finer-grained voltage drop trajectories; an index of discharge drop rate is introduced to evaluate the voltage drop trend within a short period: if the rate exceeds the threshold during detection, an early warning is initiated and marked as a potential risk record. This strategy achieves the improvement of the recognition accuracy of sub-healthy state power supplies while maintaining the detection efficiency, and discovers potential performance degradation hazards in advance through trend analysis of voltage changes.

[0093] When the start-stop frequency approaches the limit (such as more than once per hour), and the load mutation is severe (such as the load fluctuation exceeds 70% of the system's maximum carrying capacity), the system will determine that the current is in a high-risk scenario. Typical applications include occasions such as emergency departure, wind and wave state change, and abnormal switching of power subsystems.

[0094] Strategy content: Start the high-frequency detection mechanism. The detection frequency can be as high as once every 2 hours. The simulated load is set to 80% - 100% of the rated output to simulate the actual sudden discharge demand; the sampling frequency is increased to 10 Hz or even higher to accurately capture the voltage fluctuations in seconds. Multivariate analysis is introduced during the detection process, such as instantaneous voltage drop value, drop rate, and integral of fluctuation amount. Once the system detects that the index exceeds the limit, it will trigger a fault warning and recommend immediately switching to the backup power supply or performing emergency maintenance. This strategy reflects the strongest state of real-time dynamic response ability, sacrificing certain system resources in exchange for the highest safety guarantee for the battery discharge performance.

[0095] By establishing a detection strategy selection mechanism based on the risk level, the marine containerized battery power detection system realizes an adaptive detection process strongly coupled with the operating scenario. This solution effectively makes up for the defects of the traditional detection system, such as a single detection period, rough detection granularity, and lagging response, enabling the system to achieve "early warning, dynamic tracking, and intelligent control" in critical working conditions. The strategy grading mechanism also significantly improves the battery life management ability, avoids triggering frequent detections in unnecessary scenarios, and reduces the occupation of system resources and the usage loss of the battery.

[0096] After detecting the battery power supply through the detection strategy, to identify whether there is a discharge voltage drop in the battery power supply, the following steps are included:

[0097] After performing a discharge test on the battery power supply through the detection strategy, the system needs to deeply analyze the voltage data collected during the discharge process to identify whether there is a phenomenon of discharge voltage drop. Discharge voltage drop is an important indicator to measure the performance stability and response ability of the battery under sudden load.

[0098] During the execution of the detection strategy, the system continuously collects the voltage data at the output terminal of the battery during the discharge stage according to a preset sampling frequency (such as 1 Hz for low risk, 5 Hz for medium risk, and 10 Hz for high risk). The collected data is denoted as: V(t i ), i = 1, 2, …, N, where t i represents the i-th sampling time point, N is the total number of samples within the detection period. Combining the nominal voltage V nom of the battery, the minimum allowable operating voltage V min , and the system load, the discharge voltage drop threshold V drop_th is defined. Usually, the value is: V drop_th = V nom - γ·(V nom - V min ), where γ ∈ [0.3, 0.7] is the sensitivity coefficient, and the higher the value, the stricter the requirement. During the entire detection period, the system traverses all the voltage data to determine whether there is a voltage value V(t i ) at any time point that satisfies: V(t i ) < V drop_th . If it is satisfied, it is marked as a voltage drop occurring; otherwise, it is judged that the discharge is normal.

[0099] If there is a discharge voltage drop, a cumulative-variation analysis is performed on the discharge voltage drop (first analyze whether the discharge voltage drop exceeds the drop threshold. If it exceeds, calculate the drop rate and then perform a calculus operation on the drop rate, that is, cumulative amount and fluctuation amount analysis. Then, combine the cumulative amount and fluctuation amount to analyze the severity of the discharge voltage drop), judge the severity of the voltage drop of the battery power supply, and adaptively adjust the detection process according to the judgment result, including the following steps:

[0100] If V(t i ) < V drop_th , it is judged that there is a discharge voltage drop and the drop rate is calculated. The expression is:

[0101]

[0102] where r drop is the drop rate, V(t s ) is the starting voltage of the drop, V(t m ) is the voltage at the lowest voltage point, t s is the starting point of the drop, and t m is the lowest voltage point. This rate represents the amplitude of voltage drop per unit time and is an important parameter reflecting the degradation speed of the battery's power supply ability with sudden changes in load.

[0103] To analyze the "change trend" of the entire voltage change process, the system integrates the rate during the voltage dip process to obtain the total voltage dip amount, which represents the amplitude of the total voltage loss. The expression is:

[0104] In the formula, S drop is the amplitude of the total voltage loss, T is the detection duration, and r drop (t) is the voltage dip rate at time t. The larger the amplitude of the total voltage loss, the more energy is lost during the entire voltage dip process, and the weaker the short-term support ability of the battery.

[0105] Further analyze the degree of fluctuation in the voltage dip curve, that is, examine whether there are severe oscillations or unstable responses in the voltage response. Calculate the amplitude of the voltage dip fluctuation. The expression is: In the formula, W vol is the amplitude of the voltage dip fluctuation, M is the number of monitoring points, is the voltage dip rate at the i-th monitoring point, is the average value of the voltage dip rates. The smaller the amplitude of the voltage dip fluctuation, the smaller the fluctuation of the voltage dip rate within the detection duration.

[0106] If the amplitude of the total voltage loss is greater than or equal to the loss threshold and the amplitude of the voltage dip fluctuation is less than or equal to the fluctuation threshold, it is analyzed that the voltage dip is severe within the detection duration, and the sampling frequency in the current scenario needs to be increased by 10%;

[0107] If the amplitude of the total voltage loss is greater than or equal to the loss threshold and the amplitude of the voltage dip fluctuation is greater than the fluctuation threshold, it is analyzed that the voltage dip is medium within the detection duration (the voltage dip rate at some monitoring points may be less than the loss threshold), and the sampling frequency in the current scenario needs to be increased by 5%;

[0108] If the amplitude of the total voltage loss is less than the loss threshold, it is analyzed that the voltage dip is good within the detection duration, and no adjustment is required.

[0109] Embodiment 3: In this embodiment, a scoring function can also be obtained by weighted calculation of the start-stop frequency and the load mutation index, and then the scenario is divided according to the scoring function. The specific scheme is as follows:

[0110] To realize the intelligent adaptive function of the marine containerized battery power supply detection system, the system first calculates the risk level based on two core indicators: the start-stop frequency and the load mutation intensity index. The start-stop frequency F startstop refers to the number of starts and stops of the ship's power system within a unit time, usually counted per hour or per day. The load mutation intensity index L jump reflects the degree of mutation of the current still rate in the main power system during the instantaneous start-stop process. After the two indicators are standardized, they are mapped to a unified risk scoring function. The function expression is: Among them, R scene is the risk score, α and β are weight factors, and α + β = 1. In this application, α = 0.4 and β = 0.6 determine the influence weights of frequency and mutation on the overall risk level; F max is the maximum start-stop frequency, and L max is the maximum load mutation intensity index. According to the result of R scene , the system divides the scenarios into three levels: low-risk scenarios, medium-risk scenarios, and high-risk scenarios, corresponding to different detection strategies respectively.

[0111] Compare the obtained risk score with a preset first score threshold and a second score threshold, and the first score threshold is less than the second score threshold. If the risk score is less than or equal to the first score threshold, classify the current scenario as a low-risk scenario. If the risk score is greater than the first score threshold and less than or equal to the second score threshold, classify the current scenario as a medium-risk scenario. If the risk score is greater than the second score threshold, classify the current scenario as a high-risk scenario.

[0112] The scenario division is carried out by means of weighted calculation, considering more comprehensively and accurately.

[0113] Example 4: Please refer to Figure 1 As shown, the marine container-type battery power detection system in this embodiment includes a data processing module, a detection strategy generation module, and an adaptive adjustment module;

[0114] Data processing module: Automatically collect the actual start-stop operation data of the ship, record the time points of each start and stop, and at the same time, monitor the load change of the main power system at the moment of start and stop in real time, analyze the acquisition results, and construct a start-stop frequency statistical chart and a load mutation mode model. The start-stop frequency statistical chart and the load mutation mode model are used as dynamic input sources, and the dynamic input sources are sent to the detection strategy generation module;

[0115] Detection strategy generation module: After determining the current scenario based on the dynamic input source, analyze whether it is necessary to start the fast discharge capacity detection process. If so, automatically generate a battery power detection strategy based on the current scenario, and send the battery power detection strategy to the adaptive adjustment module;

[0116] Adaptive adjustment module: After detecting the battery power through the detection strategy, identify whether there is a discharge voltage drop in the battery power. If so, perform a cumulative-variation analysis on the discharge voltage drop (first analyze whether the discharge voltage drop exceeds the drop threshold. If it does, calculate the drop rate and then perform a calculus operation on the drop rate, that is, cumulative amount and fluctuation amount analysis, and then combine the cumulative amount and fluctuation amount to analyze the severity of the discharge voltage drop), judge the severity of the voltage drop of the battery power, and adaptively adjust the detection process according to the judgment result. The adjusted detection process is fed back to the detection strategy generation module.

[0117] It should be understood that the term "and / or" in this document is merely a description of the association relationship of associated objects, indicating that three relationships can exist. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. Additionally, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, and specific understanding can be made by referring to the context before and after.

[0118] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not imply the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0119] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in this document can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0120] As described above, the above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for detecting a shipboard containerized battery power supply, characterized in that: The detection method includes the following steps: After the detection system constructs the start-stop frequency statistical chart and the load mutation mode model, it determines the current scenario, and analyzes whether it is necessary to start the discharge capacity detection process based on the determination result of the current scenario; If necessary, automatically generate a battery power detection strategy based on the current scenario, and after detecting the battery power through the detection strategy, identify whether there is a discharge voltage drop in the battery power; If it exists, perform cumulative-variation analysis on the discharge voltage drop, judge the severity of the voltage drop of the battery power, and adaptively adjust the detection process according to the judgment result.

2. The method for detecting a marine containerized storage battery power supply according to claim 1, characterized in that: Performing cumulative-variation analysis on the discharge voltage drop to judge the severity of the voltage drop of the battery power includes the following steps: If V(t i ) < V drop_th , it is determined that there is a discharge voltage drop, and the drop rate is calculated. The expression is as follows: Integrate the drop rate during the drop process to obtain the amplitude of the total voltage loss, and the expression is: In the formula, S drop is the amplitude of the total voltage loss, T is the detection duration, and r drop (t) is the drop rate at time t; Calculate the amplitude of the drop fluctuation, and the expression is: In the formula, W vol is the amplitude of the drop fluctuation, M is the number of monitoring points, is the drop rate at the i-th monitoring point, is the average value of the drop rate; If the total voltage loss amplitude is greater than or equal to the loss threshold and the drop fluctuation amplitude is less than or equal to the fluctuation threshold, analyze that the voltage drop is severe during the detection duration; If the total voltage loss amplitude is greater than or equal to the loss threshold and the drop fluctuation amplitude is greater than the fluctuation threshold, analyze that the voltage drop is medium during the detection duration; If the total voltage loss amplitude is less than the loss threshold, analyze that the voltage drop is good during the detection duration.

3. The marine container-type battery power detection method according to claim 2, wherein: Identifying whether there is a discharge voltage drop in the battery power includes the following steps: During the execution of the detection strategy, voltage data at the output terminal of the battery during the discharge stage is continuously collected at a preset sampling frequency, and the collected data is denoted as: V(t i ), i = 1, 2, …, N, where t i represents the i-th sampling time point, N is the total number of samplings within the detection period. All voltage data is traversed to determine whether there exists a voltage value V(t i ) that satisfies: V(t i ) < V drop_th . If it is satisfied, it is marked as a voltage dip occurring; otherwise, it is judged as normal discharge. Here, V drop_th is the voltage dip threshold for discharge.

4. The marine container-type battery power detection method according to claim 1, characterized in that: Determining the current scenario, and analyzing whether it is necessary to start the discharge capacity detection process based on the determination result of the current scenario, including the following steps: Calculate the risk level based on the start-stop frequency and the load mutation intensity index. The start-stop frequency F startstop refers to the number of starts and stops of the ship's power system per unit time. The load mutation intensity index L jump reflects the degree of mutation of the current still rate in the main power system during the instantaneous start-stop process. After standardizing the start-stop frequency and the load mutation intensity index, the risk score is calculated through the risk scoring function; Compare the obtained risk score with a preset first score threshold and a second score threshold, and the first score threshold is less than the second score threshold; If the risk score is less than or equal to the first score threshold, classify the current scenario as a low-risk scenario. If the risk score is greater than the first score threshold and less than or equal to the second score threshold, classify the current scenario as a medium-risk scenario. If the risk score is greater than the second score threshold, classify the current scenario as a high-risk scenario.

5. The marine containerized battery power detection method according to claim 1, characterized in that: Determining the current scenario, and analyzing whether it is necessary to start the discharge capacity detection process based on the determination result of the current scenario further includes the following steps: Count the number of start-stop times per unit time from the time series to obtain the start-stop frequency; After monitoring the power change rate during each start-stop through the load mutation mode model, calculate the load mutation intensity index; Combining the start-stop frequency and the load mutation index, the scene level is divided into: Low-risk scenario: If F startstop <F th ∧L jump <L th , do not start the fast discharge capacity detection process; Medium-risk scenario: If F startstop ≥F th ∧L jump <L th , regularly initiate the fast discharge capacity detection process; High-risk scenario: F startstop ≥ F th ∧ L jump ≥ L th , initiate the fast discharge capacity detection process; Among them, F startstop is the start-stop frequency, L jump is the load mutation intensity index, F th is the frequency threshold, L th is the index threshold.

6. The method for detecting a marine container-type battery power supply according to claim 5, wherein: Statistically count the number of start-stop operations within a unit time from the time series to obtain the start-stop frequency. The expression is as follows: Among them, F startstop is the start-stop frequency, and N startstop is the total number of start-stop operations recorded within the statistical time window T window ; T window is the statistical time window.

7. The method for detecting a marine containerized battery power supply according to claim 6, characterized in that: After monitoring the power change rate during each start-stop operation through the load mutation mode model, the load mutation intensity index is calculated, and the expression is: In the formula, L jump is the load mutation intensity index, N startstop is the total number of start-stop operations recorded within the statistical time window T window , and ΔP rate represents the power change rate during the i-th start-stop operation.

8. The marine container-type battery power detection method according to claim 7, wherein: Constructing the load mutation mode model includes the following steps: The detection system collects the voltage, current, active power, and reactive power of the main power system; By recording the start-stop control signals of the ship, automatically determine the time points of each start or stop action, and set an analysis time window to capture the evolution trajectory before and after the load disturbance. The time window is: [t0 - T1, t0 + T2], where: t0 is the time stamp of the start-stop event, T1 is the pre-analysis period before start-stop, and T2 is the load response period after start-stop; Extract the rate characteristics of the load change from the power curve to reflect the mutation intensity of the load per unit time, and generate the load mutation mode model. The expression is: Where: ΔP rate represents the power change rate, δ is the time interval, and P(t) is the instantaneous active power.

9. The marine container-type battery power detection method according to claim 8, wherein: Constructing the start-stop frequency statistical chart includes the following steps: Record the time stamp for each start-stop operation, label it as the start time or stop time, and attach the corresponding status change flag, including the power on / off state and the main load on / off state; Statistically count the start-stop times within a set fixed time window or sliding time window, count the start-stop events within each time window, calculate the number of starts, stops, and start-stop ratios within this time window, and form frequency statistics data; Based on the frequency statistics data, draw a start-stop frequency statistical graph, with the horizontal axis being the time axis and the vertical axis being the start-stop frequency value within the corresponding time window.

10. A shipboard container-type battery power detection system for implementing the detection method according to any one of claims 1-9, characterized in that: It includes a data processing module, a detection strategy generation module, and an adaptive adjustment module; Data processing module: Automatically collect the actual start-stop operation data of the ship, record the time points of each start and stop, and at the same time, real-time monitor the load change of the main power system at the moment of start and stop, analyze the acquisition results, construct a start-stop frequency statistical graph and a load mutation mode model, and the start-stop frequency statistical graph and the load mutation mode model 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 fast discharge capacity detection process. If so, automatically generate a battery power detection strategy based on the current scenario; Adaptive adjustment module: After detecting the battery power through the detection strategy, identify whether there is a discharge voltage drop in the battery power. If there is, perform cumulative-variable analysis on the discharge voltage drop, judge the severity of the voltage drop of the battery power, and adaptively adjust the detection process according to the judgment result.

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