An energy control system and method for marine applications

Abstract

The application discloses a kind of energy control systems and methods for ship application, including: high-voltage main circuit control module, data acquisition module, intelligent control module, expansion and protection module, multi-stage power electronic conversion module and heat dissipation module.High-voltage main circuit control module contains high-voltage contactor, fuse and pre-charge circuit;Data acquisition module contains current sensor, temperature sensor and battery management system interface;Intelligent control module contains BMS module control and communication unit, for executing energy optimization algorithm with the goal of minimizing energy consumption, maximizing efficiency, and interacting with the upper energy management system;Expansion and protection module contains high-voltage connector, busbar and metal box, for multiple energy module access;Multi-stage power electronic conversion module is electrically connected to high-voltage DC bus, based on energy optimization results to adjust duty cycle and output power;Heat dissipation module is linked with temperature sensor, and key components are cooled to maintain operating temperature.

Description

Technical Field

[0001] This invention relates to the field of marine energy management technology, and specifically to an energy control system and method for marine applications. Background Technology

[0002] As the global shipping industry transforms towards low-carbon and intelligent operation, ship energy systems are upgrading from traditional single-engine diesel engines to a comprehensive energy supply model that integrates lithium batteries, supercapacitors, and diesel generators to meet the demands of reducing carbon emissions, improving energy efficiency, and adapting to complex operating conditions (such as startup, cruising, and berthing). However, existing ship energy control systems have revealed the following technical bottlenecks in practical applications:

[0003] I. Insufficient efficiency and weak dynamic adaptation capability in multi-energy coordinated control.

[0004] Traditional systems often employ fixed power allocation strategies, making it difficult to respond in real time to the highly nonlinear and abrupt changes in ship propulsion loads (affected by speed, propeller speed, and draft). Furthermore, key parameters such as the state of health (SOH) and state of charge (SOC) of energy modules (e.g., lithium batteries and supercapacitors) lack dynamic monitoring and optimization scheduling mechanisms, resulting in low energy conversion efficiency (below 80% under some operating conditions) and energy redundancy (backup energy modules are often inefficiently operating).

[0005] II. Insufficient surge suppression upon startup

[0006] In existing energy control systems, when the ship's high-voltage DC bus is started, the instantaneous charging of the bus capacitor will generate a surge current impact.

[0007] Third, limited accuracy in data acquisition and processing affects control decisions.

[0008] (1) Severe interference with current data: Strong electromagnetic interference (such as propellers and inverters) during the operation of the ship's high-voltage system causes high-frequency noise in the raw data of the current sensor. Traditional fixed window filtering methods are difficult to balance steady-state smoothness and dynamic response, which directly affects the calculation accuracy of energy optimization algorithms.

[0009] (2) Large temperature monitoring error: The nonlinear characteristics of sensors such as NTC thermistors and K-type thermocouples introduce the original error, and the fluctuation of ambient temperature in the high-voltage control box causes the "component surface temperature" measurement value to include environmental interference;

[0010] (3) Inaccurate battery state assessment: Traditional state of charge (SOC) calculation relies on a single ampere-hour integration method or open circuit voltage (OCV) correction at a fixed temperature, and does not consider the impact of battery aging (SOH decrease) on OCV characteristics, leading to misjudgment of energy dispatch.

[0011] IV. Poor adaptability of energy optimization algorithms

[0012] Traditional PID control relies solely on adjusting the output based on the current error, lacks the ability to anticipate future load changes, and struggles to dynamically adjust the priority of energy modules according to operating conditions, resulting in low energy utilization efficiency.

[0013] In summary, existing ship energy control systems have significant shortcomings in terms of multi-energy collaborative optimization, startup reliability, safety protection redundancy, data acquisition accuracy, and energy dispatch adaptability. There is an urgent need for a new energy control system and method that can achieve efficient energy dispatch, highly reliable pre-charge control, multi-level active protection, and accurate processing of multi-source data. Summary of the Invention

[0014] In order to overcome the shortcomings of the prior art, the present invention aims to provide an energy control system and method for marine applications, which solves the technical problems of existing marine energy control systems in terms of multi-energy coordination efficiency, start-up reliability, safety protection redundancy, data acquisition accuracy and energy dispatch adaptability.

[0015] To solve the above problems, the technical solution adopted by the present invention is as follows:

[0016] An energy control system for marine applications includes:

[0017] The high-voltage main circuit control module includes a high-voltage contactor, a fuse, and a pre-charging circuit. The high-voltage contactor is used for main circuit on / off control, the fuse is used for overcurrent protection, and the pre-charging circuit includes a current-limiting resistor, an auxiliary contactor, and a control logic circuit for pre-charging during system startup.

[0018] The data acquisition module includes a current sensor, a temperature sensor, and a battery management system interface, which are used to acquire main circuit current, key component temperature, and battery status data, respectively.

[0019] The intelligent control module, including the BMS module control and communication unit, is configured to execute an energy optimization algorithm with the objective function of minimizing system energy consumption and maximizing energy conversion efficiency, and to interact with the upper-level energy management system.

[0020] The expansion and protection module, including high-voltage connectors, busbars, and a metal enclosure, is used for the connection of multiple energy modules;

[0021] A multi-stage power electronic conversion module is electrically connected to the high-voltage DC bus and is configured to adjust the duty cycle and output power based on energy optimization results;

[0022] The heat dissipation module, in conjunction with the temperature sensor, dissipates heat from key components to maintain the system's operating temperature.

[0023] An energy control method for marine applications includes the following steps:

[0024] The system starts up, and multiple energy modules are connected through the expansion and protection modules and communication protocols. The bus capacitor is precharged using the current-limiting resistor of the pre-charging circuit.

[0025] The main circuit current data is monitored in real time by a current sensor, the temperature data of key components is collected by a temperature sensor, and the battery cell voltage, temperature, state of charge and health data are obtained through the battery management system interface.

[0026] Based on the collected data, the BMS module controls and the communication unit to execute the dynamic energy optimization algorithm. Combined with the multi-stage power electronic conversion module, the energy is dispatched to the 1000V high-voltage DC bus to complete the coordination of charging and discharging strategies and voltage fluctuation control.

[0027] When an overcurrent, overvoltage, or overtemperature abnormality is detected, the main circuit is quickly cut off by the high-voltage contactor, and the fuse is triggered to isolate the fault based on the cut-off result.

[0028] Preferably, when monitoring current data in real time, the following are included:

[0029] The current sensor is connected in series on the busbar of the high-voltage main circuit to collect current data in real time at a preset sampling frequency.

[0030] The collected current data is transmitted to the intelligent control module in real time. After processing by an adaptive moving average filtering strategy, including dynamic noise level assessment, adaptive adjustment of filter parameters, operating condition correlation optimization, and abnormal data isolation, the data is used for dynamic calculation of energy optimization algorithm and real-time determination of overcurrent faults.

[0031] Preferably, when collecting temperature data, the following are included:

[0032] The temperature sensors are integrated onto the surfaces of the high-voltage contactor contacts, the fuse core, and the current-limiting resistor of the pre-charging circuit, and temperature data is collected in real time at a preset sampling frequency.

[0033] The collected temperature data is transmitted to the intelligent control module in real time. After piecewise linearization correction and ambient temperature compensation, it is used to trigger the start of the heat dissipation module and serves as the basis for judging over-temperature faults.

[0034] The piecewise linearization correction and ambient temperature compensation process includes: piecewise linearization correction of nonlinear characteristics, dynamic compensation of ambient temperature field, and self-correction of thermal resistance aging.

[0035] Preferably, when acquiring battery cell data, the process includes:

[0036] The battery cell voltage is measured using a 16-bit high-precision analog-to-digital converter built into the BMS.

[0037] The temperature of individual battery cells is collected using an NTC thermistor sensor integrated into the BMS.

[0038] The state of charge is calculated by combining the ampere-hour integration method with open-circuit voltage correction.

[0039] The battery health status is comprehensively assessed by combining internal resistance increment analysis and capacity decay model.

[0040] The acquired data on battery cell voltage, temperature, state of charge, and health are transmitted in real time to the intelligent control module through the battery management system interface. After CRC verification and outlier filtering, the data are used in the energy optimization algorithm.

[0041] The calculation of the state of charge includes: dynamic stable condition identification and correction triggering, construction of temperature-compensated OCV-SOC mapping table, aging-adaptive OCV correction, OCV denoising under vibration disturbance, multi-stage correction weight allocation, and SOC error closed-loop correction.

[0042] Preferably, when comprehensively assessing health status, the following are included:

[0043] Sinusoidal current signals are injected at selected characteristic frequency points, and the battery terminal voltage response is acquired synchronously. The AC impedance at each frequency point is calculated using fast Fourier transform.

[0044] Calculate the rate of increase of AC impedance relative to the initial value at each frequency point, and construct an internal resistance aging correlation model based on the rate of increase and weighting coefficients;

[0045] The temperature acceleration factor is calculated based on the Arrhenius equation, and the equivalent full charge-discharge cycle number is obtained by using the acceleration factor and the actual number of cycles.

[0046] A capacity decay model is constructed based on the equivalent number of full charge-discharge cycles, the initial capacity, and the decay constant.

[0047] The weighting coefficients of the internal resistance aging correlation model and the capacity decay model are dynamically adjusted based on the current SOH value, and the final health status is output.

[0048] Preferably, the dynamic energy optimization algorithm adopts a model predictive control framework, and calculates the optimal energy allocation ratio of each energy module within the optimization cycle by real-time input of main circuit current, key component temperature, battery cell state of charge, health status and bus voltage data, and generates corresponding MPC instructions.

[0049] Bidirectional energy conversion between the energy module and the bus is achieved through an isolated bidirectional DC-DC converter, and the duty cycle is adjusted according to MPC commands.

[0050] The DC energy of the bus is converted into AC power available to the load through a grid-connected DC-AC inverter, and the output power is adjusted according to the MPC command.

[0051] The model prediction control framework includes: a dynamic load prediction model that dynamically adjusts the prediction time domain length based on the load change rate; a multi-objective weight dynamic allocation strategy; embedding of lifetime constraints and temperature safety boundaries; collaborative optimization of multiple energy modules; and online model self-updating.

[0052] Preferably, when an overcurrent, overvoltage, or overtemperature abnormality is detected, the high-voltage contactor is triggered to disconnect first, and the fuse countdown is started simultaneously.

[0053] If the high-voltage contactor fails to disconnect, the fuse will blow within a set time to achieve backup isolation; if the high-voltage contactor successfully disconnects, the fuse will remain inactive.

[0054] Preferably, when the system starts up, it scans the connected energy modules through broadcast frames and receives the device descriptors fed back by the energy modules;

[0055] The intelligent control module detects the voltage range, communication protocol compatibility, and protection level of the energy module;

[0056] After successful matching, a unique device ID is assigned to the energy module, and the physical connection with the high-voltage DC bus is completed by closing the auxiliary contactor.

[0057] Preferably, when the system starts up, the auxiliary contactor is closed to connect the current-limiting resistor in series to the main circuit;

[0058] The bus capacitor begins to charge through the current-limiting resistor, and the control logic circuit monitors the voltage across the capacitor in real time.

[0059] When the capacitor voltage or pre-charge time reaches the set value, the control logic circuit determines that the pre-charge is complete, closes the main circuit high-voltage contactor, and simultaneously opens the auxiliary contactor to complete the main circuit switching.

[0060] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0061] I. The efficiency of multi-energy coordinated control is significantly improved, reducing system energy consumption and fluctuations.

[0062] A dynamic energy optimization algorithm based on the Model Predictive Control (MPC) framework is used to predict future load demands by combining real-time data such as ship speed and propeller speed. The target weights of "minimizing energy consumption" and "maximizing efficiency" are dynamically adjusted according to startup / cruising / berthing conditions. At the same time, based on the characteristics of lithium batteries, supercapacitors, and diesel generators, a condition-related priority matrix is ​​designed to achieve dynamic power allocation among multiple energy modules.

[0063] II. Enhanced surge suppression capability and significantly improved system reliability.

[0064] The design includes a pre-charging circuit with current-limiting resistors, auxiliary contactors, and control logic circuits. It monitors the rise rate of the bus capacitor voltage and the pre-charging time in real time, and completes the main circuit switching when the capacitor voltage or pre-charging time reaches the preset value. This design effectively suppresses the starting inrush current.

[0065] III. Improved multi-level active safety protection mechanism and enhanced fault isolation redundancy.

[0066] A coordinated control strategy of "high-voltage contactor priority disconnection + fuse backup isolation" is adopted: when the high-voltage contactor detects overcurrent, overvoltage or overtemperature abnormality, it cuts off the main circuit; if the contactor fails to disconnect (such as contact sticking), the fast fuse blows within 5ms to achieve backup protection.

[0067] IV. The accuracy of multi-source data acquisition and processing has been greatly improved, supporting precise control decisions.

[0068] Current data: An adaptive moving average filtering strategy is adopted, which improves the noise suppression effect under steady-state conditions, shortens the response delay under dynamic conditions, effectively removes abnormal spikes, and provides reliable input for energy optimization algorithms.

[0069] Temperature data: Through piecewise linearization correction, dynamic compensation for ambient temperature, and self-correction due to thermal resistance aging, the accuracy of temperature monitoring of key components is improved, providing a reliable basis for heat dissipation control and over-temperature fault judgment.

[0070] Battery Status: The multi-method SOC calculation (ampere-hour integration + temperature-compensated OCV correction + aging adaptive correction) significantly reduces the error in state of charge calculation; the multi-stage weighted SOH assessment (internal resistance incremental method + capacity decay model) provides a more accurate battery health status, providing precise data support for energy dispatch and lifetime prediction.

[0071] V. Optimization of energy dispatch adaptability and multi-objective balancing capabilities to meet the needs of complex operating conditions.

[0072] The dynamic load prediction model under the MPC framework (combining data such as ship speed and propeller speed) can predict load changes in advance, enabling control commands to adapt to operating conditions proactively. Through dynamic weight allocation based on operating conditions, it solves the problem of conflicting multiple objectives in traditional control. At the same time, it embeds battery life constraints, temperature safety boundaries, and multi-energy priority matrices to achieve a synergistic effect of "proactiveness, safety, and efficiency" in energy scheduling.

[0073] In summary, this invention significantly improves the reliability, efficiency, and safety of ship energy systems through the deep integration of system-level hardware design and intelligent algorithms, providing key technical support for the green and intelligent transformation of ships.

[0074] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments. Attached Figure Description

[0075] Figure 1 This is a pre-charging circuit diagram according to an embodiment of the present invention;

[0076] Figure 2 This is a front view of the high-voltage control box according to an embodiment of the present invention;

[0077] Figure 3 This is a top view of the high-voltage control box according to an embodiment of the present invention;

[0078] Figure 4 This is a side view of the high-voltage control box according to an embodiment of the present invention;

[0079] Figure 5 This is a flowchart illustrating the steps of an energy control method for ship applications according to an embodiment of the present invention.

[0080] The following are the symbols in the attached diagram: 1. Current-limiting resistor; 2. Auxiliary contactor; 3. Control logic circuit; 4. High-voltage contactor; 5. Fuse. Detailed Implementation

[0081] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0082] At the same time, it should be understood that, for ease of description, the dimensions of the various parts shown in the accompanying drawings are not drawn according to actual scale.

[0083] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the scope of this application and its application or use.

[0084] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and equipment should be considered part of the specification.

[0085] Example 1 provides an energy control system for marine applications, including: a high-voltage main circuit control module, a data acquisition module, an intelligent control module, an expansion and protection module, a multi-stage power electronic conversion module, and a heat dissipation module.

[0086] See Figure 1The circuit diagram of the high-voltage main circuit control module is shown. The high-voltage main circuit control module includes a high-voltage contactor 4, a fuse 5, and a pre-charging circuit. The high-voltage contactor 4 is used for main circuit on / off control, the fuse 5 is used for overcurrent protection, and the pre-charging circuit includes a current-limiting resistor 1, an auxiliary contactor 2, and a control logic circuit 3, which is used for pre-charging during system startup.

[0087] The data acquisition module includes a current sensor, a temperature sensor, and a battery management system (BMS) interface, which are used to acquire main circuit current, key component temperature, and battery status data, respectively.

[0088] The intelligent control module includes a BMS module control and communication unit, supports CAN, RS485 and Ethernet protocols, and is configured to execute energy optimization algorithms, safety protection strategies and data communication with the objective function of minimizing system energy consumption and maximizing energy conversion efficiency, and interact with the upper-level energy management system (EMS).

[0089] The expansion and protection module includes a high-voltage connector, busbars, and a metal enclosure (dustproof, waterproof, corrosion-resistant, and fireproof), and adopts shielded cables and grounding design to enable multi-energy module access, electromagnetic interference suppression, and physical protection.

[0090] The multi-stage power electronic conversion module is electrically connected to the high-voltage DC bus and is configured to adjust the duty cycle and output power based on energy optimization results to achieve efficient energy conversion.

[0091] The heat dissipation module works in conjunction with the temperature sensor to dissipate heat from critical components in order to maintain the system's operating temperature.

[0092] See Figures 2 to 4 In one possible embodiment, the energy control system of this embodiment is integrated into the high-voltage control box.

[0093] In one possible embodiment, the current sensor is a Hall sensor or a shunt. The Hall sensor supports a wide measurement range of 0-500A with a measurement accuracy of ±0.5% FS (full-scale error) and a response time of ≤10ms. The shunt is made of low-impedance manganese copper alloy and is used in conjunction with a high-precision differential amplifier to achieve current signal conversion.

[0094] In one possible embodiment, the temperature sensor is an NTC thermistor or a K-type thermocouple. The NTC thermistor has a measurement range of -40℃ to 125℃, an accuracy of ±1℃, and a response time of ≤20ms. The K-type thermocouple has a measurement range of -200℃ to 800℃. The heat dissipation module is a fan or a liquid cooling system.

[0095] In one possible embodiment, the high-voltage contactor 4 is a high-voltage contactor 4 that supports 1000V DC voltage and has a main contact breaking time ≤10ms, and the fuse 5 is a fast fuse with a breaking capacity ≥20kA, whose time-current characteristics match the breaking time of the high-voltage contactor 4.

[0096] In one possible embodiment, the multi-stage power electronic conversion module includes: an isolated bidirectional DC-DC converter and a grid-connected DC-AC inverter; the isolated bidirectional DC-DC converter is used for bidirectional energy conversion between the energy module and the bus, and adjusts the duty cycle according to MPC commands; the grid-connected DC-AC inverter is used to convert the DC energy of the bus into AC load usable electrical energy, and adjusts the output power according to MPC commands.

[0097] In one possible embodiment, the high-voltage connector is a rectangular pluggable high-voltage connector (compliant with IEC 60529 standard), with an interface having an IP67 protection rating, withstanding 1000V DC voltage, and silver-plated contact surfaces (contact resistance ≤50μΩ); the high-voltage connector is equipped with a mechanical locking structure (to prevent loosening due to ship vibration), supports blind mating positioning (positioning pin deviation ≤0.5mm), and is compatible with the physical access of various types of energy modules such as lithium battery packs, supercapacitors, and diesel generator inverters.

[0098] Example 2, see Figure 5 A flowchart illustrating the steps of an energy control method for marine applications is provided in this embodiment. Figure 5 An energy control method for marine applications, as shown, includes the following steps:

[0099] Step S1: Module Access and Pre-charging: Start the system and access multiple energy modules through expansion and protection modules and communication protocols (CAN, RS485, Ethernet) to achieve compatibility expansion for large-scale deployment;

[0100] The bus capacitor is precharged by the current-limiting resistor 1 of the pre-charging circuit to suppress the surge current impact during system startup.

[0101] Step S2: Data Acquisition: Real-time monitoring of the ship's high-voltage main circuit current data using current sensors, acquisition of temperature data of key components in the high-voltage control box using temperature sensors, and acquisition of battery cell voltage, temperature, state of charge (SOC), and state of health (SOH) data through the battery management system (BMS) interface.

[0102] Step S3: Algorithm Optimization and Energy Scheduling: Based on the collected data, the dynamic energy optimization algorithm is executed through the BMS module control and communication unit. Combined with the multi-stage power electronic conversion module, energy scheduling is performed on the 1000V high-voltage DC bus to complete the coordination of charging and discharging strategies, energy allocation of different energy modules, and voltage fluctuation control (fluctuation range ≤ ±5%).

[0103] Step S4, Active Safety Protection: When an overcurrent, overvoltage, or overtemperature abnormality is detected, the main circuit is quickly cut off by the high-voltage contactor 4, and the fuse 5 is triggered to blow and isolate the fault based on the cut-off result.

[0104] In step S2 above, real-time monitoring of the ship's high-voltage main circuit current data includes:

[0105] The current sensor is connected in series on the busbar of the high-voltage main circuit to collect current data in real time at a sampling frequency of 100Hz.

[0106] The collected current data is transmitted to the intelligent control module in real time via the CAN bus at a period of ≤20ms. After being processed by an adaptive moving average filtering strategy, it is used for dynamic calculation of the energy optimization algorithm and real-time determination of overcurrent faults.

[0107] Background Description: In ship energy control systems, the real-time performance and accuracy of high-voltage main circuit current data are crucial for energy optimization algorithms and fault diagnosis. However, the complex operating environment of ships makes current data susceptible to the following interferences and challenges:

[0108] Strong noise interference: The operation of ship high-voltage systems (such as propulsion and inverters) will generate strong electromagnetic interference, which will cause high-frequency noise (such as surges and spikes) in the raw data collected by current sensors, directly affecting the dynamic calculation accuracy of energy optimization algorithms.

[0109] Dynamic changes in operating conditions: Ship operation includes multiple operating conditions such as startup (rapid rise in current), cruising (stable current), and berthing (decline in current). Different operating conditions have conflicting requirements for the "smoothness" and "real-time" of current data: stable operating conditions require noise suppression (smoothing priority), while sudden load changes require rapid response (real-time priority). Fixed window filtering methods cannot meet both requirements.

[0110] Abnormal data contamination: Ship vibration or momentary sensor malfunctions may cause abnormal spikes in current data (such as instantaneous values ​​exceeding 200% of the rated current). If these spikes are not effectively removed, they will contaminate the filtering results and falsely trigger overcurrent protection. Based on this:

[0111] In one possible embodiment, the adaptive moving average filtering strategy processing includes:

[0112] Dynamic noise level assessment: Real-time calculation of the variance σ of current data within the current sampling window. 2 and the rate of change ΔI / Δt between adjacent sampling points, where the variance σ 2 The rate of change ΔI / Δt reflects the degree of data fluctuation and the characteristics of sudden current changes.

[0113] Adaptive adjustment of filter parameters: when variance σ2 > Set a threshold (e.g., 5A) 2 When the rate of change |ΔI / Δt| is greater than the set threshold (e.g., 20A / ms) (corresponding to sudden changes in ship load or strong electromagnetic interference scenarios), increase the size of the moving average window (e.g., expand from the default 5 points to 10 points) and increase the weight coefficient of historical data (e.g., set the weight of the most recent 3 points to 0.4, the middle 3 points to 0.3, and the earliest 4 points to 0.3) to enhance the suppression of high-frequency noise;

[0114] When the variance σ 2 When the change rate |ΔI / Δt| is less than or equal to the set threshold (corresponding to the stable cruise scenario of a ship), the size of the moving average window is reduced (e.g., from 5 points to 3 points), and the weight of historical data is reduced (e.g., the weight of the two most recent points is 0.6, and the weight of the earliest point is 0.4) to improve the response speed of the filtered data to changes in current.

[0115] Operating condition correlation optimization: The ship's operating status (start-up / cruising / berthing) is obtained through the intelligent control module. In the start-up condition (current rise phase), an additional feedforward compensation term is introduced (the compensation value is the current change rate of the previous cycle × the current sampling interval) to avoid the current peak lag caused by the expansion of the window; in the berthing condition (current fall phase), the compensation coefficient is reduced to prevent numerical deviation caused by overcompensation.

[0116] Abnormal data isolation: If the current value of a single sampling point exceeds the rated current by 200% and the rate of change is >50A / ms (judged as spike interference), the point is marked as an abnormal value and removed. The linear interpolation of the two points before and after is used to replace it to avoid abnormal points from contaminating the filtering results.

[0117] In this embodiment of the invention, it needs to be further explained that the above processing procedure achieves the following by dynamically adjusting the filtering parameters (window size, data weight) and combining operating condition identification and anomaly isolation:

[0118] Steady-state operating conditions (such as cruise): Suppress high-frequency noise and reduce steady-state error;

[0119] Dynamic operating conditions (such as startup): Reduce response latency and avoid peak lag;

[0120] Abnormal scenarios (such as vibration interference): Remove abnormal points to ensure data reliability.

[0121] The adaptive moving average filtering strategy provided in this embodiment is designed for current data acquisition in complex ship operating environments. It achieves a balance between "smoothness" and "real-time performance" through dynamic parameter optimization.

[0122] In step S2 above, when collecting temperature data of key components inside the high-voltage control box, the following steps are included:

[0123] Temperature sensors are integrated on the contacts of high-voltage contactor 4, the fuse core of fuse 5, and the surface of current-limiting resistor 1 in the pre-charging circuit, and temperature data is collected in real time at a sampling frequency of 50Hz.

[0124] The collected temperature data is transmitted to the intelligent control module in real time via the CAN bus at a period of ≤30ms. After piecewise linearization correction and ambient temperature compensation, it is used to trigger the start of the heat dissipation module (e.g., start heat dissipation when the temperature is ≥75℃) and serves as the basis for judging over-temperature faults (e.g., the over-temperature threshold is set to 100℃).

[0125] The temperature sensor uses an NTC thermistor or a thermocouple.

[0126] Background Description: In ship energy control systems, temperature monitoring of key components within the high-voltage control box (such as the high-voltage contactor 4 contacts, fuse 5 fuse core, and current-limiting resistor 1) is crucial for ensuring safe system operation (e.g., triggering the heat dissipation module and determining over-temperature faults). However, temperature data acquisition faces the following challenges:

[0127] The NTC thermistors and K-type thermocouples commonly used in ships exhibit significant nonlinear characteristics:

[0128] The resistance-temperature (RT) curve of NTC exhibits exponential decay, and directly inferring the temperature from the resistance value will introduce an original error of ±1℃.

[0129] The thermoelectric potential-temperature (ET) curve of a type K thermocouple is nonlinear over a wide temperature range (the Seebeck coefficient is not constant), and the original measurement error can reach ±2℃.

[0130] Interference from ambient temperature field:

[0131] The ambient temperature inside the ship's high-voltage control box is easily affected by equipment operation (such as busbar heating) and external climate (such as changes in cabin temperature), causing the "component surface temperature" collected by the sensors to include interference from the ambient temperature. For example, when the ambient temperature rises from 25°C to 40°C, the measured value of the contact surface temperature may be overestimated by 1.5°C due to ambient heat conduction (assuming a thermal resistance of 0.1°C / W), failing to accurately reflect the component's own temperature rise (the actual temperature may only rise by 10°C, but the measured value shows an increase of 11.5°C).

[0132] Thermal resistance drift caused by component aging:

[0133] After long-term operation, oxidation of the contacts in high-voltage contactor 4 and aging of the fuse element in fuse 5 will change the equivalent thermal resistance (R_th) between the components and the environment. For example, the initial thermal resistance of the contacts may be 0.1℃ / W, but after 1000 hours of operation, it may increase to 0.2℃ / W. If the thermal resistance parameter is not dynamically corrected, the ambient temperature compensation model will gradually become ineffective, and the measurement error will accumulate over time, threatening the long-term reliability of the system. Based on this:

[0134] In one possible embodiment, the piecewise linearization correction and ambient temperature compensation processing includes:

[0135] Piecewise linearization correction of nonlinear characteristics:

[0136] If it is an NTC thermistor, based on its resistance-temperature (RT) characteristic curve (B-value coefficient 3435K, resistance value 10kΩ at 25℃), the measurement range (-40℃~125℃) is divided into 3 intervals: low temperature region (-40℃~25℃), medium temperature region (25℃~80℃), and high temperature region (80℃~125℃). Within each interval, the RT data of 3 sets of standard temperature points (e.g., -40℃, 0℃, 25℃; 25℃, 50℃, 80℃; 80℃, 100℃, 125℃) are fitted using the least squares method, and linearization equations are established respectively. After correction, the measurement error is optimized from ±1℃ to ±0.5℃.

[0137] For type K thermocouples, based on their thermoelectric potential-temperature (ET) characteristics (Seebeck coefficient approximately 41 μV / ℃), the measurement range (-200℃~800℃) is divided into four intervals (-200℃~0℃, 0℃~400℃, 400℃~600℃, 600℃~800℃). By using a lookup table combined with linear interpolation (e.g., in the 0℃~400℃ interval, five standard points are taken: 0℃ (0mV), 100℃ (4.096mV), 200℃ (8.138mV), 300℃ (12.209mV), and 400℃ (16.397mV), respectively), the nonlinear thermoelectric potential signal is converted into a linear temperature value. After correction, the error is optimized from ±2℃ to ±1℃.

[0138] Dynamic compensation of ambient temperature field:

[0139] Three auxiliary temperature sensors (using the same type of NTC or thermocouple as the measured point) are installed in non-heating areas inside the high-voltage control box (such as the side wall of the metal box and the non-current-carrying section of the busbar) to collect the ambient temperature T in real time. env1 T env2 T env3 Calculate the average ambient temperature T envavg = (T env1 +T env2 +T env3 ) / 3;

[0140] A model for the influence of ambient temperature was established to investigate the thermal conductivity characteristics of the key components under test (contact 4 of the high-voltage contactor, fuse 5, and current-limiting resistor 1).

[0141] T measured =T actual +R_th×(T envavg -Tref );

[0142] Among them, T measured T represents the original measurement value from the sensor. actual R_th represents the actual temperature of the component, and R_th represents the equivalent thermal resistance between the component and the environment (contact R_th1 = 0.1℃ / W, fused core R_th2 = 0.08℃ / W, resistance R_th3 = 0.15℃ / W). ref The initial ambient temperature of the system (25℃);

[0143] By calculating T in real time actual =T measured -R_th×(T envavg -T ref This eliminates the interference of ambient temperature fluctuations on the measured values ​​(e.g., when the ambient temperature rises from 25℃ to 40℃, the contact measurement value can be compensated for by a decrease of 1.5℃ (0.1℃ / W×15℃)).

[0144] Thermal resistance self-correction due to aging:

[0145] The intelligent control module records the system's cumulative operating time t (in hours) and the historical temperature data of the measured component, and fits the equivalent thermal resistance R using the least squares method. th The coefficient of change of thermal resistance parameters with time t, k (e.g., k = 0.001℃ / (W·h)), is used to dynamically update the thermal resistance parameters.

[0146] R_th(t) = R_th initial + k×t;

[0147] Among them, R_th initial R_th1 is the initial thermal resistance. initial R_th2 is the initial thermal resistance of the contact. initial R_th3 is the initial thermal resistance of the fused core. initial The initial thermal resistance of the resistor;

[0148] (For example, the initial thermal resistance of the contact R_th1) initial =0.1℃ / W, after running for 1000 hours, it is updated to R_th1(1000)=0.1+0.001×1000=0.2℃ / W), to compensate for the increased thermal resistance caused by contact oxidation and aging of the molten core.

[0149] In this embodiment of the invention, it is necessary to further explain that the above processing utilizes nonlinear piecewise correction to linearize the sensor characteristic curve piecewise, and reduces the original measurement error through the least squares method or table lookup method; auxiliary sensors are used to collect the ambient temperature inside the chamber, a heat conduction model is established, and the component's own temperature rise and environmental interference are separated; running time and historical temperature data are recorded, and thermal resistance parameters are dynamically updated to avoid compensation failure due to component aging. In summary, through error correction, interference isolation, and model self-updating, the steady-state accuracy and dynamic response capability of temperature data are improved, providing a reliable basis for heat dissipation control and over-temperature fault determination, and ensuring the safe and efficient operation of the energy control system.

[0150] In step S2 above, acquiring data on battery cell voltage, temperature, state of charge, and state of health includes:

[0151] The battery cell voltage is measured using the BMS's built-in 16-bit high-precision analog-to-digital converter (ADC), with a measurement range of 2.5V~4.2V (compatible with lithium-ion batteries), a measurement accuracy of ±10mV, and a single-cell sampling interval of ≤100ms.

[0152] The temperature of a battery cell is collected by an NTC thermistor sensor integrated into the BMS. The NTC thermistor sensor is attached to the surface of the cell one by one, with a measurement range of -20℃ to 60℃, an accuracy of ±2℃, and a sampling frequency of 5Hz.

[0153] The state of charge (SOC) is calculated by combining the ampere-hour integration method with open-circuit voltage (OCV) correction.

[0154] The state of health (SOH) is comprehensively assessed by combining incremental internal resistance analysis (based on AC impedance method) and capacity decay model (combining cycle number and temperature stress data).

[0155] The acquired battery cell voltage, temperature, state of charge (SOC), and state of health (SOH) data are transmitted to the intelligent control module in real time via the battery management system (BMS) interface using the CAN bus communication protocol (supporting 500kbps rate) or the Ethernet protocol (supporting 100Mbps rate) at a period of ≤50ms. After CRC verification and outlier filtering, the data are used for adjusting the charge and discharge power of the energy optimization algorithm and triggering the active safety protection strategy.

[0156] Background: In marine energy control systems, State of Charge (SOC) is a core parameter for battery energy management, directly impacting energy dispatch strategies (such as charge / discharge power allocation), safety protection (such as avoiding overcharge and over-discharge), and lifespan prediction. However, the operating environment of marine batteries is complex, and traditional single SOC calculation methods (such as the ampere-hour integration method or the OCV method) have significant limitations.

[0157] The cumulative error problem of the ampere-hour integration method:

[0158] The ampere-hour integration method calculates the state of charge (SOC) by integrating the current, offering strong real-time performance, but errors accumulate over time (such as current sensor accuracy deviations and the temperature dependence of coulombic efficiency η). When ship batteries operate for extended periods (such as continuous cruising for several hours), the error may exceed 5%, leading to misjudgments in energy dispatching (such as prematurely activating diesel generators due to a misjudgment of insufficient remaining power).

[0159] The effect of temperature on the OCV-SOC relationship:

[0160] Battery OCV changes significantly with temperature (e.g., the OCV of a lithium-ion battery at -20°C is about 0.2V lower than that at 25°C). If an OCV-SOC mapping table at a fixed temperature is used directly, the SOC correction error under low or high temperature conditions may exceed 10%, leading to the risk of overcharging (e.g., the SOC is misjudged as too high at low temperatures, and the battery continues to discharge even when the actual capacity is insufficient).

[0161] Changes in OCV characteristics due to battery aging:

[0162] When the state of battery health (SOH) decreases, the slope of the OCV-SOC curve decreases and the intercept shifts (e.g., when SOH < 80%, the OCV corresponding to the same SOC is 0.1V lower than that of a new battery). If the OCV mapping table is not dynamically corrected, the SOC calculation error of the aged battery will increase as SOH decreases, affecting the life management strategy (e.g., misjudging the remaining battery life).

[0163] Voltage interference caused by ship vibration:

[0164] Mechanical vibrations during ship navigation can cause instantaneous fluctuations in battery terminal voltage, directly affecting the accuracy of OCV measurements. Traditional filtering methods (such as fixed-window averaging) may over-smooth the effective signal or retain interference noise, leading to deviations in OCV correction values. Therefore:

[0165] In one possible embodiment, calculating the state of charge includes:

[0166] Dynamic stable operating condition identification and correction triggering:

[0167] The intelligent control module monitors the battery current and operating status in real time, and triggers the OCV calibration process when the following conditions are met:

[0168] The battery is in a static or low-current condition (charge / discharge current ≤ 0.1C, where C is the battery's rated capacity) for a duration of ≥ 2 minutes (to eliminate the influence of load fluctuations on the terminal voltage).

[0169] Or, an alarm for cumulative error of ampere-hour integration (if the cumulative running time is ≥15 minutes and has not been calibrated, a forced calibration will be triggered);

[0170] Construction of temperature-compensated OCV-SOC mapping table:

[0171] Pre-stored OCV-SOC baseline curves at different temperatures (-20℃, 0℃, 25℃, 40℃, 60℃), and using the cell temperature T collected by the BMS, generate the OCV-SOC mapping relationship at the current temperature using cubic spline interpolation:

[0172] OCV cal (T) = a·T 3 + b·T 2 + c·T + d;

[0173] Among them, the coefficients a, b, c, and d are determined by fitting the reference curve data of adjacent temperature points;

[0174] Aging-adaptive OCV correction:

[0175] The OCV-SOC mapping table is dynamically updated based on the battery state of health (SOH):

[0176] When SOH ≥ 90% (battery healthy), the original baseline curve is used;

[0177] When 80%≤SOH<90% (mild aging), the OCV value is negatively offset (offset = 0.05V×(90%-SOH)).

[0178] When SOH < 80% (severe aging), the OCV-SOC curve is refitted based on historical cycle data (updated every 24 hours) (slope reduced by 0.8 times, intercept shifted by 0.1V).

[0179] OCV noise reduction under vibration interference:

[0180] The OCV measurement values ​​were smoothed using a 5-point moving average filter (sampling frequency 5Hz). If the voltage fluctuation of a single sampling point was >50mV (determined to be instantaneous interference caused by ship vibration), the point was discarded and replaced by linear interpolation of the two points before and after it. The standard deviation of the filtered OCV value was ≤10mV and was considered a valid measurement value.

[0181] Multi-stage correction weight allocation:

[0182] The weighting coefficients α (0≤α≤1) of the ampere-hour integral and OCV correction are dynamically adjusted according to the battery operation stage:

[0183] At the end of charging (current ≤ 0.05C and lasting for 1 minute): α = 0.8 (OCV correction value is preferred);

[0184] Discharge end stage (current ≤ 0.05C and lasts for 1 minute): α = 0.7 (combining OCV and integral value);

[0185] Stable cruise phase (current 0.2C~0.5C): α=0.3 (mainly based on ampere-hour integral, with OCV as auxiliary correction);

[0186] SOC error closed-loop correction:

[0187] Calculate the state of charge (SOC) using ampere-hour integration. amp = SOC initial + (∫I·ηdt) / C n SOC amp The state of charge (SOC) is calculated using ampere-hour integration. initial Let I be the initial state of charge, I be the battery charging / discharging current, η be the coulombic efficiency (η=0.98 during charging and η=0.95 during discharging), dt be the time element (integration time interval), and C be the initial state of charge. n This refers to the battery's rated capacity.

[0188] OCV-corrected state of charge (SOC) ocv = f(OCV cal (T), SOH); SOC ocv Let f(˙) be the OCV-corrected state of charge, and f(˙) be the mapping function between OCV and SOC. cal (T) is the open-circuit voltage after temperature compensation, T is the temperature of a single battery cell, and SOH is the battery health state.

[0189] Final SOC = α·SOC ocv + (1-α)·SOC amp .

[0190] In this embodiment of the invention, it is necessary to further explain that the above calculation process is designed for complex scenarios such as dynamic operating conditions, wide temperature range, aging characteristics and vibration interference of ship batteries. Through the fusion of multiple methods and adaptive adjustment of parameters, it achieves high-precision real-time calculation of SOC, providing a reliable basis for energy optimization scheduling (such as the coordinated power supply of lithium batteries and supercapacitors) and active safety protection (such as over-discharge protection) of energy control systems.

[0191] Background: In marine energy control systems, battery state of health (SOH) is a core indicator for measuring the degree of battery performance degradation, directly affecting energy dispatch strategies (such as limiting the charging and discharging power of aging batteries), lifespan prediction (determining replacement cycles), and safety protection (preventing malfunctions caused by aging batteries). However, the operating environment of marine batteries is complex, and existing single assessment methods have the following significant limitations:

[0192] The most direct indicator of aging is the ratio of the battery's actual capacity to its initial capacity, but this requires the battery to be fully charged and discharged (which takes several hours), making it difficult to obtain in real time during the dynamic operation of a ship.

[0193] Aging can be assessed by measuring changes in the AC impedance of the battery (increased internal resistance reflects aging phenomena such as electrode polarization and electrolyte decay). It can be measured online (by injecting a small current signal), but the change in internal resistance is not significant in the early stages of aging (the increment is <5% when SOH≥90%), resulting in insufficient assessment accuracy.

[0194] Furthermore, a single method is insufficient to cover multi-dimensional aging characteristics (such as capacity decay reflecting loss of active material, and increased internal resistance reflecting ion transport barriers), necessitating the use of complementary methods. Simultaneously, battery aging can be divided into multiple stages, requiring adjustments to the weighting of evaluation methods at different stages to avoid over-reliance on internal resistance in the early stages (leading to large errors) or neglecting internal resistance (a key indicator) during severe aging. Based on this:

[0195] In one possible embodiment, the comprehensive assessment of health status includes:

[0196] Multi-frequency AC impedance method for incremental internal resistance analysis:

[0197] Sinusoidal current signals are injected at selected characteristic frequency points, and the battery terminal voltage response is acquired synchronously. The AC impedance at each frequency point is calculated by Fast Fourier Transform (FFT).

[0198] Calculate the rate of increase of AC impedance relative to the initial value at each frequency point, and construct an internal resistance aging correlation model based on the rate of increase and weighting coefficients;

[0199] A capacity decay model coupled with temperature stress and cycle count:

[0200] The temperature acceleration factor is calculated based on the Arrhenius equation, and the equivalent full charge-discharge cycle number is obtained by using the acceleration factor and the actual number of cycles.

[0201] A capacity decay model is constructed based on the equivalent number of full charge-discharge cycles, the initial capacity, and the decay constant.

[0202] Multi-stage dynamic weighted fusion evaluation:

[0203] The weighting coefficients β (0≤β≤1) of the internal resistance aging correlation model and the capacity decay model are dynamically adjusted based on the current SOH value:

[0204] When SOH ≥ 90% (initial aging): β = 0.3 (capacity decay model dominates, internal resistance change is not significant);

[0205] When 80%≤SOH<90% (mid-term aging): β=0.6 (weight balance between internal resistance increment and capacity model).

[0206] When SOH < 80% (severe aging): β = 0.8 (increased internal resistance dominates, capacity decay rate slows down but internal resistance increases significantly).

[0207] Final SOH = β×SOH imp + (1-β)×SOH cap ;

[0208] Among them, SOH imp Health status (SOH) assessed by the internal resistance aging association model, SOH cap The state of health (SOH) is assessed for the capacity decay model.

[0209] In this embodiment of the invention, it is necessary to further explain that the above-mentioned comprehensive evaluation process injects a sinusoidal current signal (covering high to low frequencies), calculates the impedance increment at each frequency point through FFT (reflecting different aging mechanisms: high-frequency impedance reflects ohmic internal resistance, mid-frequency impedance reflects charge transfer impedance, and low-frequency impedance reflects diffusion impedance), constructs an internal resistance aging correlation model, and realizes online and rapid evaluation; calculates the temperature acceleration factor based on the Arrhenius equation (quantifying the promoting effect of temperature on aging), and obtains the equivalent full charge-discharge cycle number by combining the actual cycle number, constructs a capacity decay model, and reflects the long-term aging cumulative effect; adjusts the weight of the two methods according to the current SOH value: the capacity model is the main one in the early stage, the weight is balanced in the middle stage, and the internal resistance model is the main one in the severe aging stage, matching the aging dominant mechanism at different stages; the above technical features realize high-precision and real-time evaluation of SOH of ship batteries under multi-factor aging, multi-stage heterogeneity, and complex operating environment.

[0210] In step S3 above, the dynamic energy optimization algorithm adopts a model predictive control (MPC) framework with the objective function of minimizing system energy consumption and maximizing energy conversion efficiency. By inputting real-time main circuit current, key component temperature, battery cell state of charge (SOC), state of health (SOH), and bus voltage data, it calculates the optimal energy allocation ratio of each energy module within the optimization cycle and generates corresponding MPC instructions (such as the duty cycle adjustment value of the DC-DC converter and the output power target of the inverter).

[0211] Bidirectional energy conversion between the energy module and the bus is achieved through an isolated bidirectional DC-DC converter, and the duty cycle is adjusted according to MPC commands to suppress bus voltage fluctuations (≤±5%); DC-AC inverters are used to convert DC energy from the bus into AC power available to the load, and the output power is adjusted according to MPC commands.

[0212] After executing the MPC command, the bus voltage and output power are collected in real time and fed back to the Model Predictive Control (MPC) framework to correct the optimization calculation for the next cycle.

[0213] Background Description: In ship energy control systems, energy dispatching needs to consider complex requirements such as dynamic load response, multi-objective optimization, equipment safety, and multi-energy coordination. Traditional fixed-parameter control methods (such as PID control) have the following drawbacks:

[0214] The load is highly dynamic, and traditional control systems have a lag in response.

[0215] Ship propulsion load is affected by factors such as speed, propeller speed, and draft, exhibiting strong nonlinear and abrupt characteristics. Traditional PID control relies on adjusting the output based on the current error, lacking prediction of future load changes, which can easily lead to excessive bus voltage fluctuations or unbalanced power distribution of energy modules.

[0216] Conflicting demands in multi-objective optimization:

[0217] Energy dispatching requires simultaneously minimizing system energy consumption and maximizing energy conversion efficiency, but these two priorities conflict under different operating conditions:

[0218] Start-up operations require a rapid response (efficiency takes priority), and excessive energy saving may lead to insufficient power;

[0219] Cruise operation needs to be optimized in a balanced way (both energy consumption and efficiency are important).

[0220] When operating at port, it is necessary to reduce the energy consumption of auxiliary equipment (with an emphasis on energy conservation).

[0221] A fixed-weight objective function cannot dynamically balance these contradictions.

[0222] Strict constraints exist regarding equipment lifespan and safety:

[0223] Battery aging (decreased SOH) leads to a decline in charge and discharge capacity. If its power limit is not limited, it may accelerate aging or cause overcharging and over-discharging. The risk of overheating of key components such as high voltage contactor 4 contacts (≤90℃) and current limiting resistor 1 (≤150℃) needs to be avoided in advance. Traditional control relies only on real-time threshold trigger protection and lacks predictive constraints on future temperatures.

[0224] The characteristics of different energy modules vary greatly:

[0225] The energy control system provided by this invention integrates energy modules such as lithium batteries (high energy density, suitable for continuous power supply), supercapacitors (high power density, suitable for instantaneous response), and diesel generators (backup, high noise). These modules differ significantly in power characteristics, response speed, and operating costs. Traditional control systems struggle to dynamically adjust module priorities based on operating conditions (e.g., prioritizing supercapacitors during rapid acceleration), leading to low energy utilization efficiency. Therefore:

[0226] In one possible embodiment, the model predictive control (MPC) framework includes:

[0227] Dynamic load forecasting model:

[0228] Based on real-time ship navigation data (ship speed v, propeller speed n, draft d) and historical operating database, a propulsion power prediction model is established using the recursive least squares method:

[0229] P load (k+τ) = a·v(k+τ) 3 + b·n(k+τ) 2 + c·d(k+τ) + d0;

[0230] Among them, P load (k+τ) represents the predicted propulsion load power at the τth time step in the future, where k is the current discrete time step (reference time), τ is the predicted time step size, v(k+τ) represents the predicted ship speed at the τth time step in the future, n(k+τ) represents the predicted propeller speed at the τth time step in the future, d(k+τ) represents the predicted ship draft at the τth time step in the future, and a, b, c, and d0 are the ship hydrodynamic coefficients fitted based on the navigation data of the past 30 days.

[0231] Wherein, the prediction time domain length T p Dynamically adjust based on load change rate: when |dP load When / dt|>50kW / s (load sudden change), T p =3×100ms=300ms; when |dP load When / dt|≤50kW / s (steady-state condition), T p =5 × 100 ms = 500 ms);

[0232] Multi-objective weight dynamic allocation strategy:

[0233] The weighting coefficients ω1 and ω2 (ω1+ω2=1) of minimizing system energy consumption (J1) and maximizing energy conversion efficiency (J2) in the objective function are dynamically adjusted according to the ship's operating conditions (start-up / cruising / berthing):

[0234] Start-up conditions (speed v≤5 knots, duration≤2 minutes): Prioritize rapid response, ω1=0.3 (energy consumption weight), ω2=0.7 (efficiency weight), to avoid insufficient power due to excessive pursuit of energy saving;

[0235] Cruise conditions (5 knots < v ≤ 12 knots, duration ≥ 10 minutes): Balanced optimization, ω1 = 0.5, ω2 = 0.5, taking into account both long-term energy consumption and real-time efficiency;

[0236] Port berthing condition (v≤3, duration≤5 minutes): Focus on energy saving, ω1=0.7, ω2=0.3, reduce energy consumption of auxiliary equipment;

[0237] The objective function is J = ω1˙J1 + ω2˙J2, where:

[0238] J1 = Σ(τ=1 to T) p ) [P loss (τ)](P loss (This is the sum of the conversion losses of each energy module).

[0239] J2 = Σ(τ=1 to T) p ) [η avg (τ) / η max ](η avg For average efficiency, η max (to maximize module efficiency).

[0240] Lifetime constraints and temperature safety boundary embedding:

[0241] Battery charge / discharge power constraint: P batt (τ)≤P max ×(1 - (100% - SOH) / 100%), P batt (τ) represents the battery charging / discharging power at the τ-th time step in the future (charging is positive, discharging is negative), P max This refers to the maximum allowable charge and discharge power of the battery in a healthy state (SOH=100%). SOH represents the battery's healthy state (the lower the SOH, the lower the power limit, and the slower the aging process; for example, when SOH=80%, P...). max Reduced to 80% of the original value);

[0242] Temperature safety constraint: T contactor (τ)≤90℃,T resistor (τ)≤150℃;T contactor (τ) represents the temperature of contactor 4 at the τ-th time step in the future, T resistor (τ) represents the surface temperature of the current-limiting resistor 1 at the τth time step in the future;

[0243] Strong constraint on bus voltage: ΔV bus (τ)≤±5%;ΔV bus (τ) represents the voltage fluctuation amplitude of the high-voltage DC bus at the τth time step in the future;

[0244] Multi-energy module collaborative optimization:

[0245] Considering the differences in characteristics between lithium batteries (high energy density), supercapacitors (high power density), and diesel generators (backup), a module priority matrix is ​​designed:

[0246] Cruise operating conditions: Lithium battery (60%) + supercapacitor (30%) + diesel generator (10%, activated only when SOC < 20%).

[0247] Start-up / rapid acceleration conditions: supercapacitor (50%) + lithium battery (40%) + diesel generator (10%), utilizing the fast response characteristics of supercapacitor;

[0248] Port operation: Lithium battery (70%) + supercapacitor (20%), diesel generator off to reduce noise;

[0249] During optimization, the allocation ratio of each module is solved using the Lagrange multiplier method to ensure the total output power P. total (τ)=P load (τ)+ΔP buffer (τ); P total (τ) represents the total output power at the τ-th time step in the future, P load (τ) represents the load power demand at the τ-th time step in the future, ΔP buffer (τ) represents the buffer power of the bus capacitor;

[0250] Online model self-updating:

[0251] After every 10 optimization cycles, the deviation between the actual output power and the predicted load, ΔP = P, is used. actual -P predicted By updating the hydrodynamic coefficients a, b, c, and d0 through Kalman filtering, the prediction model can adapt to changes in ship load (such as a decrease in draft after unloading) or sea state changes (such as increased wind and waves), thus avoiding the accumulation of long-term operational errors.

[0252] In this embodiment of the invention, it is necessary to further explain that the above-mentioned Model Predictive Control (MPC) framework constructs a load prediction model using the recursive least squares method, combines it with real-time ship navigation data to predict future loads, and enables control commands to adapt to load changes in advance, avoiding excessive bus voltage fluctuations. The weights of energy consumption (J1) and efficiency (J2) are adjusted according to the ship's operating conditions (start-up / cruising / berthing) to achieve optimal control under different operating conditions. Dynamic charging and discharging power is limited based on SOH to delay aging; the future temperature of contactor contacts and current-limiting resistor 1 is limited to avoid over-temperature faults; strong constraints are placed on the bus voltage to forcibly limit voltage fluctuations and ensure the stability of load power supply. Based on the differences in characteristics of lithium batteries, supercapacitors, and diesel generators, a priority matrix related to operating conditions is designed, and the optimal allocation ratio is solved using the Lagrange multiplier method, matching the total output power to load demand. Furthermore, the prediction model automatically adapts to changes in ship load or sea state to avoid the accumulation of long-term operational errors. The aforementioned technical features, through a closed-loop mechanism of "prediction-optimization-feedback," enable forward-looking, adaptable, safe, and efficient energy dispatching, providing technical support for the stable operation and performance optimization of ship energy control systems.

[0253] In step S4 above, when an overcurrent, overvoltage, or overtemperature abnormality is detected, the intelligent control module synchronously sends a fault signal to the high-voltage contactor 4 control unit and the fuse 5 monitoring unit via the CAN bus (500kbps rate), which will trigger the high-voltage contactor 4 to disconnect first, and at the same time start the fuse 5 fuse blow countdown (5ms) to ensure that the fault energy is limited to a safe range.

[0254] After an anomaly is triggered, the intelligent control module records the fault type (overcurrent / overvoltage / overtemperature), the time of occurrence, and peak parameters (such as maximum current 900A and maximum temperature 160℃) in real time, and uploads them to the upper-level energy management system (EMS) via Ethernet interface (100Mbps rate) for subsequent fault tracing and system optimization.

[0255] Among them, a high-voltage contactor 4 supporting 1000V DC voltage is adopted. After receiving the fault signal sent by the intelligent control module, the main contact breaking time is ≤10ms, ensuring that the main circuit is cut off before the fault current rises to the peak value.

[0256] Configure a fast-acting fuse with a breaking capacity of ≥20kA, whose time-current characteristics are matched with the breaking time of the high-voltage contactor 4. If the high-voltage contactor 4 fails to break (e.g., the contacts stick together), the fuse 5 will melt within 5ms to achieve backup isolation; if the high-voltage contactor 4 breaks successfully, the fuse 5 will remain inactive to avoid unnecessary replacement.

[0257] Abnormal detection conditions: Overcurrent abnormality: The main circuit current value collected in real time by the current sensor exceeds 150% of the rated current of the high voltage main circuit, and continues to exceed this threshold for 3 consecutive sampling cycles.

[0258] Overvoltage anomaly: The voltage of the 1000V high-voltage DC bus monitored by the intelligent control module exceeds the rated value by +5%, and the duration of the abnormal state is ≥20ms;

[0259] Over-temperature anomaly: The temperature of the high-voltage contactor 4 contact is ≥120℃, the temperature of the fuse 5 fuse core is ≥150℃, or the surface temperature of the pre-charge circuit current-limiting resistor 1 is ≥180℃ (triggered at any position), and the temperature exceeds the corresponding threshold for two consecutive sampling cycles.

[0260] In step S1 above, when the system starts up, it scans the connected energy modules through a broadcast frame (CAN ID: 0x001) and receives the device descriptor (including energy type, rated power, voltage range, and communication protocol priority) fed back by the energy modules.

[0261] The intelligent control module detects the voltage range of the energy module (which must cover the bus voltage), communication protocol compatibility (supporting at least one built-in system protocol), and protection level (≥IP65). If the matching fails, it will refuse access and report a "module incompatibility" fault.

[0262] After successful matching, a unique device ID is assigned to the energy module (e.g., 0x0101 represents lithium battery pack No. 1), and the physical connection with the high-voltage DC bus is completed by closing auxiliary contactor 2.

[0263] In step S1 above, when the system starts, the main circuit high-voltage contactor 4 is in the open state or the initial voltage of the bus capacitor is ≤10% of the rated voltage. The intelligent control module detects the "start pre-charge" command sent by the upper-level energy management system (EMS) and triggers the pre-charge process:

[0264] The intelligent control module sends a closing command to the auxiliary contactor 2, and the auxiliary contactor 2 completes the closing within 5ms, connecting the current-limiting resistor 1 in series to the main circuit;

[0265] The bus capacitor starts charging through the current-limiting resistor 1, and the control logic circuit 3 monitors the voltage across the capacitor in real time with a sampling frequency of 100Hz.

[0266] When the capacitor voltage reaches 90% of the bus rated voltage or the pre-charge time reaches 500ms (whichever triggers first), the control logic circuit 3 determines that the pre-charge is complete.

[0267] The intelligent control module sends a closing command to the main circuit high-voltage contactor 4 (main contactor closing time ≤ 15ms) and simultaneously sends a disconnection command to the auxiliary contactor 2 (auxiliary contactor 2 disconnection time ≤ 8ms) to complete the main circuit switching.

[0268] If the capacitor voltage rise rate is detected to be less than 0.5V / ms during the pre-charging process (which may be due to an open circuit in the current limiting resistor 1 or capacitor failure), the control logic circuit 3 will immediately disconnect the auxiliary contactor 2 and report a "pre-charging failure" fault to the upper-level energy management system (EMS) to avoid surge impact caused by the malfunction of the main circuit high-voltage contactor 4.

[0269] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An energy control method for shipboard applications, characterized by, The method comprises the following steps: Starting the system, accessing the multi-energy module through the extension and protection module and the communication protocol, and pre-charging the bus capacitor by using the current-limiting resistor of the pre-charging circuit; Real-time monitoring of main loop current data by current sensor, acquisition of key component temperature data by temperature sensor, and acquisition of battery cell voltage, temperature, state of charge and state of health data by battery management system interface; Based on the collected data, the dynamic energy optimization algorithm is executed by the BMS module control and communication unit, and the energy of the 1000V high-voltage DC bus is scheduled by combining multi-stage power electronic conversion modules, so as to complete the coordination of charging and discharging strategies, the energy distribution of different energy modules, and the voltage fluctuation control; When overcurrent, overvoltage or overtemperature abnormalities are detected, the main loop is quickly cut off by the high-voltage contactor, and the fuse is triggered to fuse and isolate the fault according to the cut-off result; Wherein, when acquiring battery cell data, it includes: Measuring battery cell voltage by the 16-bit high-precision analog-to-digital converter built-in BMS; Acquiring battery cell temperature by the NTC thermistor sensor integrated in BMS; Calculating state of charge by ampere-hour integration method combined with open circuit voltage correction; Comprehensively evaluating the state of health by analyzing the internal resistance increment and the capacity attenuation model; The acquired battery cell voltage, temperature, state of charge and state of health data are transmitted in real time to the intelligent control module through the battery management system interface, and after CRC check and abnormal value filtering processing, they are used for energy optimization algorithm; Wherein, when calculating the state of charge, it includes: dynamic stable working condition recognition and correction trigger, construction of temperature compensation OCV-SOC mapping table, aging adaptive OCV correction, OCV denoising processing under vibration interference, multi-stage correction weight distribution and SOC error closed loop correction; Comprehensively evaluating the state of health includes: Selecting a characteristic frequency point to inject a sinusoidal current signal, synchronously collecting battery terminal voltage response, and calculating the AC impedance of each frequency point by fast Fourier transform; Calculating the increment rate of the AC impedance of each frequency point relative to the initial value, and constructing the internal resistance aging correlation model based on the increment rate and the weight coefficient; Calculating the temperature acceleration factor based on the Arrhenius equation, and obtaining the equivalent full charge and discharge cycle number by using the acceleration factor and the actual cycle number; Based on the equivalent full charge and discharge cycle number, the initial capacity and the decay constant, a capacity attenuation model is constructed; According to the current SOH value, the weight coefficients of the internal resistance aging correlation model and the capacity attenuation model are dynamically adjusted, and the final state of health is output; The execution of the control method is based on the following control system, which includes: The high-voltage main loop control module includes a high-voltage contactor, a fuse and a pre-charging circuit, wherein the high-voltage contactor is used for main loop on-off control, the fuse is used for overcurrent protection, and the pre-charging circuit includes a current-limiting resistor, an auxiliary contactor and a control logic circuit, which is used for pre-charging during system startup; The data acquisition module includes a current sensor, a temperature sensor and a battery management system interface, which are used to acquire main loop current, key component temperature and battery state data, respectively; The intelligent control module includes a BMS module control and communication unit, configured to execute an energy optimization algorithm with a target function of minimizing system energy consumption and maximizing energy conversion efficiency, and interact with an upper energy management system; The expansion and protection module includes a high-voltage connector, a bus bar, and a metal box, used for the access of multiple energy modules; The multi-stage power electronic conversion module is electrically connected to the high-voltage DC bus, configured to adjust the duty cycle and output power based on the energy optimization results; The heat dissipation module is linked with the temperature sensor to dissipate heat from key components to maintain system operating temperature.

2. The energy control method for shipboard applications according to claim 1, characterized in that, Real-time monitoring of current data includes: The current sensor is installed in series on the bus bar of the high-voltage main circuit to collect current data in real time at a preset sampling frequency; The collected current data is transmitted to the intelligent control module in real time, and after processing using an adaptive moving average filtering strategy including noise level dynamic evaluation, filtering parameter adaptive adjustment, working condition correlation optimization, and abnormal data isolation, it is used for dynamic calculation of the energy optimization algorithm and real-time determination of overcurrent faults.

3. The energy control method for marine vessel applications according to claim 1, characterized in that, Temperature data collection includes: The temperature sensor is integrated on the surface of the high-voltage contactor contact, the fuse core, and the current limiting resistor of the pre-charge circuit, and real-time temperature data is collected at a preset sampling frequency; The collected temperature data is transmitted to the intelligent control module in real time, and after processing by piecewise linearization correction and environmental temperature compensation, it is used to trigger the start of the heat dissipation module and as a basis for determining over-temperature faults; The piecewise linearization correction and environmental temperature compensation processing includes non-linear characteristic piecewise linearization correction, environmental temperature field dynamic compensation, and thermal resistance aging self-correction.

4. The energy control method for marine vessel applications according to claim 1, characterized in that, The dynamic energy optimization algorithm uses a model predictive control framework to calculate the optimal energy distribution ratio of each energy module within the optimization period by inputting real-time main circuit current, key component temperature, battery cell state of charge, health status, and bus voltage data, and generates corresponding MPC instructions; Bidirectional energy conversion between energy modules and the bus is performed through an isolated bidirectional DC-DC converter, and the duty cycle is adjusted according to the MPC instructions; The bus DC power is converted to AC load usable power through a grid-connected DC-AC inverter, and the output power is adjusted according to the MPC instructions; The model predictive control framework includes a dynamic load prediction model that dynamically adjusts the prediction time domain length based on the load change rate, a multi-objective weight dynamic distribution strategy, life constraints and temperature safety boundaries embedded, multi-energy module collaborative optimization, and online model self-update.

5. The energy control method for marine vessel applications according to claim 1, characterized in that, When overcurrent, overvoltage, or over-temperature anomalies are detected, the high-voltage contactor is preferentially triggered to break, and the fuse is simultaneously started to count down; If the high-voltage contactor fails to break, the fuse will melt within the set time to achieve backup isolation, and if the high-voltage contactor successfully breaks, the fuse will remain inactive.

6. The energy control method for marine vessel applications according to claim 1, characterized in that, When the system starts, it scans the accessed energy modules through broadcast frames and receives the device descriptors feedback from the energy modules; The intelligent control module detects the voltage range, communication protocol compatibility, and protection level of the energy modules; After the matching, a unique device ID is assigned to the energy module, and the physical connection with the high-voltage DC bus is completed through the closed auxiliary contactor.

7. The energy control method for marine vessel applications according to claim 1, characterized in that, When the system starts, the auxiliary contactor is closed, and the current-limiting resistor is connected in series with the main circuit. The bus capacitor starts to charge through the current-limiting resistor, and the control logic circuit monitors the voltage across the capacitor in real time. When the capacitor voltage or pre-charge time reaches the set value, the control logic circuit determines that the pre-charge is complete, the high-voltage contactor of the main circuit is closed, and the auxiliary contactor is opened, completing the switching of the main circuit.

8. An energy control system for marine vessel applications, characterized by, During operation, the method of claim 1 is executed, including: A high-voltage main circuit control module, including a high-voltage contactor, a fuse, and a pre-charge circuit, wherein the high-voltage contactor is used for main circuit on-off control, the fuse is used for overcurrent protection, and the pre-charge circuit includes a current-limiting resistor, an auxiliary contactor, and a control logic circuit, which is used for pre-charge during system startup; A data acquisition module, including a current sensor, a temperature sensor, and a battery management system interface, which are used to collect main circuit current, key component temperature, and battery state data, respectively; An intelligent control module, including a BMS module control and communication unit, configured to execute an energy optimization algorithm with the objective function of minimizing system energy consumption and maximizing energy conversion efficiency, and interact with the upper energy management system; An expansion and protection module, including a high-voltage connector, a bus bar, and a metal box, which is used for the access of multiple energy modules; A multi-stage power electronic conversion module, electrically connected with the high-voltage DC bus, configured to adjust the duty cycle and output power based on the energy optimization results; A heat dissipation module, linked with the temperature sensor, which dissipates heat from key components to maintain system operating temperature.

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