A Large-Model-Based Energy Efficiency Management Method for Electric Tugs

Abstract

This invention discloses an energy efficiency management method for electric tugboats based on a large model, comprising the following steps: S1, dynamic energy allocation optimization: optimizing dynamic energy allocation based on a large model according to the real-time load of the electric tugboat; S2, charging strategy optimization: optimizing the charging time and charging capacity of the electric tugboat according to the price gradient of the power grid; S3, battery system safety protection: monitoring the battery temperature, voltage, and current in real time and providing early warning protection for the battery; S4, propulsion system safety assurance: monitoring the operating data of the propulsion system in real time and protecting, monitoring, and diagnosing faults in the propulsion system; S5, power supply system safety redundancy: monitoring and regulating the power quality of the DC power distribution system and providing emergency power supply to the AC power distribution system; This method achieves precise power allocation, intelligent charging management, efficient safety protection, and reliable power supply assurance, improving the energy efficiency and operational safety of electric tugboats.

Description

Technical Field

[0001] This invention belongs to the field of data processing technology, specifically relating to an energy efficiency management method for electric tugboats based on a large model. Background Technology

[0002] In recent years, significant progress has been made in energy efficiency management technology for electric tugboats both domestically and internationally. Regarding dynamic energy allocation, simulation modeling and optimization algorithms are being used to deeply explore energy management systems. For example, China Shipbuilding Power (Group) Co., Ltd. has successfully applied DC 1000V integrated electric propulsion technology to improve energy efficiency. In terms of charging strategy optimization, foreign ports adopt charging strategies based on real-time load prediction, while domestic ports are exploring fast charging technology to reduce the impact of charging on operational efficiency. Regarding lithium battery system safety protection technology, lithium iron phosphate batteries have become mainstream, and active cooling and passive protection technologies are being developed to improve battery safety. The safety assurance of propulsion and power supply systems is also gradually moving towards intelligence, leveraging sensor networks and real-time monitoring systems to improve reliability and redundancy.

[0003] However, current technologies still face challenges in adapting to complex operating conditions, intelligent charging equipment, and efficient charging of multiple tugboats. Specifically, existing technologies have the following shortcomings: 1) Dynamic energy allocation: Existing energy management systems mostly rely on preset parameters for optimization, making it difficult to adapt to fluctuations in energy demand under complex operating conditions in real time, resulting in poor dynamic optimization capabilities; poor coupling and coordination between propulsion, energy storage, and charging equipment systems lead to low energy allocation efficiency; and the impact of environmental factors such as temperature and humidity on battery performance is not fully considered, lowering overall energy efficiency. 2) Charging strategy optimization: Current charging equipment has a low level of intelligence, mostly providing fixed power output, and cannot dynamically adjust charging parameters based on battery status; charging interfaces and protocols of different ports and tugboat manufacturers are incompatible, lacking a unified charging standard, which limits the universality and flexibility of charging equipment; during busy port periods, the long charging time for electric tugboats becomes a bottleneck restricting their continuous operation. 3) Lithium-ion battery system safety protection: Batteries are at risk of thermal runaway at high temperatures or when overcharged, and existing protection technologies are insufficient to cope with extreme conditions; battery status monitoring systems lack real-time performance and accuracy, making it difficult to predict the remaining battery life and health status; existing technologies mostly focus on passive protection, such as cooling systems and overcharge protection, lacking proactive intervention methods to avoid potential risks in advance. 4) Propulsion system safety assurance: Existing sensors have limited measurement accuracy in complex sea conditions, affecting the control effect of the propulsion system; some electric tugboat propulsion systems have insufficient redundancy, and failure of key components can easily lead to serious safety accidents; propulsion system control strategies are mostly based on traditional algorithms, with limited intelligence and lack of adaptive capabilities to cope with sudden operating conditions. 5) Power supply system safety redundancy: Existing redundancy designs are based on complex circuits, increasing the difficulty of system maintenance and failure rate; power supply system fault diagnosis technology is immature, making it difficult to quickly locate and repair problems; energy loss occurs in the energy distribution and conversion of the power supply system, resulting in low energy management efficiency. Summary of the Invention

[0004] To address the aforementioned issues, this invention proposes an energy efficiency management method for electric tugboats based on a large model. This method achieves precise power allocation, intelligent charging management, efficient safety protection, and reliable power supply, thereby improving the energy efficiency and operational safety of electric tugboats and providing technical support for their widespread application in port operations and other fields.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] An energy efficiency management method for electric tugboats based on a large model includes the following steps:

[0007] S1. Dynamic Energy Allocation Optimization: Based on the real-time load of the electric tugboat, the dynamic energy allocation is optimized using a large model.

[0008] S2. Charging strategy optimization: Optimize the charging time and charging amount of electric tugboats based on the price gradient of the power grid;

[0009] S3. Battery system safety protection: Real-time monitoring of battery temperature, voltage and current, and early warning protection for the battery;

[0010] S4. Safety assurance of the propulsion system: Real-time monitoring of the operation data of the propulsion system, protection of the propulsion system, fault monitoring and fault diagnosis;

[0011] S5. Safety redundancy of power supply system: Monitor and regulate the power quality of DC power distribution system, and provide emergency power supply for AC power distribution system.

[0012] Preferably, the specific process of step S1 is as follows:

[0013] S11. Based on a large model, predict and analyze the total power of electric tugboats under different operating scenarios. To increase motor power Power of other devices The sum of ;

[0014] S12. The large model learns from historical operational data of the electric tugboat and analyzes in real time the optimal ratio of propulsion motor power to total power. , Historical operational data includes load weight, sailing speed, water current, and wind direction.

[0015] S13, Propulsion motor power based on analysis of allocated power. Power allocated to other equipment Dynamic adjustment This value enables dynamic power allocation.

[0016] Preferably, step S1 further includes battery pack charge-discharge equalization, the specific process of which is as follows:

[0017] S14. The large model monitors the charging and discharging state of the battery pack in real time, assuming the initial state of charge of each battery pack is... ;in, , representing 8 groups of batteries;

[0018] S15. During the discharge process, predict the discharge current of each battery group based on the real-time load. and discharge time 1. Considering the battery capacity Calculate the elapsed discharge time The state of charge of each battery group after step 1 is calculated using the following formula: ,in, The state of charge of the i-th battery group;

[0019] S16. Execute the battery balancing algorithm program to adjust the discharge current of each battery group to keep the state of charge of each battery group consistent and reduce the accumulation of performance differences between battery groups; when it is found that the state of charge of a certain battery group is lower than the average value, the amount of reduction in the discharge current of this battery group is calculated based on the large model to achieve battery balancing management.

[0020] Preferably, the specific process of step S2 is as follows:

[0021] S21, in slow charging mode, the large model combines the initial battery charge. Battery capacity and slow charging current The formula for predicting the time required to fully charge in slow charging mode is as follows: ,in, The time required to fully charge in slow charging mode;

[0022] S22, in fast charging mode, the large model monitors the battery temperature in real time. ,Voltage and current Establish the charging current variation function By integrating the charging current change function, the time required for a full charge in fast charging mode is predicted. The calculation formula is as follows: ,in, The time required to fully charge in fast charging mode;

[0023] S23. During the charging process, assume the electricity price during off-peak hours is... Peak hour electricity price is , By combining electricity price information and charging data during peak and off-peak hours, the charging amount of electric tugboats is monitored in real time, with the charging amount during off-peak hours being... During peak hours, the charging volume is Calculate the total cost of charging the tugboat. ;

[0024] S24. Based on a large model, considering the relationship between grid load and tugboat charging power, as well as electricity price differences, the tugboat charging power is dynamically adjusted through an optimization algorithm. Historical electric tugboat electricity demand data is analyzed to determine the optimal charging amount during off-peak hours. This minimizes charging costs.

[0025] Preferably, the specific process of step S3 is as follows:

[0026] S31. The battery temperature data is transmitted to the large model in real time via a temperature sensor. The large model analyzes the battery temperature data in real time. Let the critical temperature at which battery thermal runaway occurs be... When the battery temperature In case of thermal runaway, the blocking protection mechanism is immediately activated, cutting off the connection between batteries through smart fuses or electronic switches; during thermal runaway, the heat conduction equation is used. By combining real-time monitored battery temperature data, the risk of thermal runaway can be predicted in advance; among them, Density of battery materials; Specific heat capacity of battery materials; Thermal conductivity; The internal heat generation rate of the battery;

[0027] S32. Collect and monitor the voltage and current of individual battery cells in real time. For overvoltage faults, assume the normal operating voltage range of the battery is... When the voltage of a single cell is monitored in real time satisfy When an overvoltage fault is detected, the connection between the batteries is disconnected via a smart fuse or electronic switch; for overcurrent faults, assuming the battery's rated current is... When the current of a single cell is monitored in real time satisfy When an overcurrent fault is detected in the battery, the connection between the batteries is cut off via a smart fuse or electronic switch.

[0028] S33. Construct a Rint model, including the battery's terminal voltage. With current Battery internal resistance and open circuit voltage The relationship is Utilizing the data analysis and learning capabilities of large models, the least squares method is used to analyze model parameters and determine battery health status and faults; among them, it is assumed that the collected data... Find the objective function based on the group terminal voltage and corresponding current data. smallest The value is calculated using the following formula: ,in, Let be the terminal voltage of the j-th battery group; Let be the current of the j-th battery group;

[0029] right Seeking information about The derivative of , and set it to 0: Solving the equation yields an estimated value for the battery's internal resistance R.

[0030] Preferably, the specific process of step S4 is as follows:

[0031] S41. Overload protection for permanent magnet propulsion motor: Real-time monitoring of the operating power of the permanent magnet propulsion motor. and rated power ,when When an overload is detected in the permanent magnet propulsion motor, the large model immediately activates the motor's protection function, cutting off the power supply within milliseconds to protect the permanent magnet propulsion motor and the entire propulsion system from damage; among which, Overload factor, The large model dynamically adjusts the overload coefficient based on the real-time operating status and historical data of the permanent magnet propulsion motor. ;

[0032] S42. Fault monitoring of the propulsion system: The propulsion system is based on a programmable logic controller (PLC) and transmits the status data of the electric propulsion equipment to a large model. When the monitored status data exceeds the normal range, it is determined that the electric propulsion equipment is operating abnormally and an alarm signal is immediately issued. The status data includes the current, voltage and speed of the propulsion motor.

[0033] S43. Fault diagnosis of propulsion system: Construct a diagnostic model based on fault data in historical data, pre-learn the motor's operating data under different fault states, and when the motor speed difference exceeds the threshold, the diagnostic model combines other motor parameters monitored in real time, matches and analyzes them in the knowledge base to obtain the motor fault diagnosis result.

[0034] Preferably, in step S5, the specific process of monitoring and regulating the power quality of the DC power distribution system is as follows:

[0035] S51. Real-time monitoring of the actual voltage of the DC bus in the DC power distribution system. Rated voltage and allowable voltage fluctuation range ;

[0036] S52, when At that time, the large model initiates the adjustment mechanism, using a voltage regulation algorithm based on proportional-integral-derivative control to control the DC bus voltage output. The calculation formula is as follows: ,in, DC bus voltage output; This is the proportionality coefficient; The integral time constant; The differential time constant; For voltage deviation, ; For DC output time; for The voltage value at that moment; For time;

[0037] S53, the large model dynamically adjusts itself by learning from historical data and analyzing real-time data. , , The value controls the power regulation device in the DC power distribution system to stabilize the DC bus voltage within the allowable range and improve the quality of the output power.

[0038] Preferably, in step S5, the specific process of providing emergency power to the AC power distribution system is as follows:

[0039] S54. Monitor the operating status of the AC power distribution system in real time. When AC power fails, control the emergency power supply to provide power to the propulsion system and critical equipment; assuming the capacity of the emergency power supply is... The output voltage is The total power requirement for propulsion systems and key equipment is The duration for which emergency power supplies can provide continuous power. ;

[0040] The S55 emergency power supply is equipped with an intelligent charging device and control system. The large-scale model automatically adjusts the output power of the emergency power supply according to real-time changes in the ship's power demand; the charging and discharging efficiency of the emergency power supply is also taken into consideration. Actual available power Actual continuous power supply time .

[0041] By adopting the above technical solution, the present invention has the following beneficial effects:

[0042] 1. This invention adopts a dynamic energy allocation mechanism to adapt to energy fluctuations under complex working conditions in real time, strengthens the coordination of propulsion, energy storage and charging equipment, comprehensively considers environmental factors, and improves dynamic optimization of energy allocation and overall energy efficiency.

[0043] 2. The charging strategy optimization of this invention is based on a large model to dynamically adjust parameters according to the battery status, unify the charging standard, solve the interface protocol compatibility problem, optimize the charging strategy for busy ports, and shorten the charging time.

[0044] 3. The safety protection of the battery system of the present invention addresses the risk of thermal runaway by constructing a monitoring system based on a large model to predict battery life and health, explore proactive measures, and avoid potential safety hazards.

[0045] 4. The propulsion system of this invention ensures safety by using a large model to optimize the propulsion system design, increase redundancy, and improve the adaptability and safety in response to sudden working conditions.

[0046] 5. The power supply system of the present invention provides safety redundancy by using large model technology to simplify redundant circuit design, reduce maintenance difficulty and faults, develop efficient fault diagnosis technology, optimize energy distribution and conversion, and improve the energy management efficiency of the power supply system. Attached Figure Description

[0047] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0048] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0049] like Figure 1 As shown, an energy efficiency management method for electric tugboats based on a large model includes the following steps:

[0050] S1. Dynamic Energy Allocation Optimization: Based on the real-time load of the electric tugboat, the dynamic energy allocation is optimized using a large model.

[0051] The specific process of step S1 is as follows:

[0052] S11. Based on a large model, predict and analyze the total power of electric tugboats under different operating scenarios. To increase motor power Power of other devices The sum of ;

[0053] S12. The large model learns from historical operational data of the electric tugboat and analyzes in real time the optimal ratio of propulsion motor power to total power. , Historical operational data includes load weight, sailing speed, water current, and wind direction.

[0054] S13, Propulsion motor power based on analysis of allocated power. Power allocated to other equipment Dynamic adjustment Values ​​that enable dynamic power allocation;

[0055] Step S1 also includes battery pack charge-discharge balancing, the specific process of which is as follows:

[0056] S14. The large model monitors the charging and discharging state of the battery pack in real time, assuming the initial state of charge of each battery pack is... ;in, , representing 8 groups of batteries;

[0057] S15. During the discharge process, predict the discharge current of each battery group based on the real-time load. and discharge time 1. Considering the battery capacity Calculate the elapsed discharge time The state of charge of each battery group after step 1 is calculated using the following formula: ,in, The state of charge of the i-th battery group;

[0058] S16. Execute the battery balancing algorithm program to adjust the discharge current of each battery group to keep the state of charge of each battery group consistent and reduce the accumulation of performance differences between battery groups; when it is found that the state of charge of a certain battery group is lower than the average value, the amount of reduction in the discharge current of this battery group is calculated based on the large model to achieve battery balancing management.

[0059] S2. Charging strategy optimization: Optimize the charging time and charging amount of electric tugboats based on the price gradient of the power grid;

[0060] The specific process of step S2 is as follows:

[0061] S21, in slow charging mode, the large model combines the initial battery charge. Battery capacity and slow charging current The formula for predicting the time required to fully charge in slow charging mode is as follows: ,in, The time required to fully charge in slow charging mode;

[0062] S22, in fast charging mode, the large model monitors the battery temperature in real time. ,Voltage and current Establish the charging current variation function By integrating the charging current change function, the time required for a full charge in fast charging mode is predicted. The calculation formula is as follows: ,in, The time required to fully charge in fast charging mode;

[0063] S23. During the charging process, assume the electricity price during off-peak hours is... Peak hour electricity price is , By combining electricity price information and charging data during peak and off-peak hours, the charging amount of electric tugboats is monitored in real time, with the charging amount during off-peak hours being... During peak hours, the charging volume is Calculate the total cost of charging the tugboat. ;

[0064] S24. Based on a large model, considering the relationship between grid load and tugboat charging power, as well as electricity price differences, the tugboat charging power is dynamically adjusted through an optimization algorithm. Historical electric tugboat electricity demand data is analyzed to determine the optimal charging amount during off-peak hours. To minimize charging costs;

[0065] S3. Battery system safety protection: Real-time monitoring of battery temperature, voltage and current, and early warning protection for the battery;

[0066] The specific process of step S3 is as follows:

[0067] S31. The battery temperature data is transmitted to the large model in real time via a temperature sensor. The large model analyzes the battery temperature data in real time. Let the critical temperature at which battery thermal runaway occurs be... When the battery temperature In case of thermal runaway, the blocking protection mechanism is immediately activated, cutting off the connection between batteries through smart fuses or electronic switches; during thermal runaway, the heat conduction equation is used. By combining real-time monitored battery temperature data, the risk of thermal runaway can be predicted in advance; among them, Density of battery materials; Specific heat capacity of battery materials; Thermal conductivity; The internal heat generation rate of the battery;

[0068] S32. Collect and monitor the voltage and current of individual battery cells in real time. For overvoltage faults, assume the normal operating voltage range of the battery is... When the voltage of a single cell is monitored in real time satisfy When an overvoltage fault is detected, the connection between the batteries is disconnected via a smart fuse or electronic switch; for overcurrent faults, assuming the battery's rated current is... When the current of a single cell is monitored in real time satisfy When an overcurrent fault is detected in the battery, the connection between the batteries is cut off via a smart fuse or electronic switch.

[0069] S33. Construct a Rint model, including the battery's terminal voltage. With current Battery internal resistance and open circuit voltage The relationship is Utilizing the data analysis and learning capabilities of large models, the least squares method is used to analyze model parameters and determine battery health status and faults; among them, it is assumed that the collected data... Find the objective function based on the group terminal voltage and corresponding current data. smallest The value is calculated using the following formula: ,in, Let be the terminal voltage of the j-th battery group; Let be the current of the j-th battery group;

[0070] right Seeking information about The derivative of , and set it to 0: Solving the equation yields an estimated value for the battery's internal resistance R;

[0071] S4. Safety assurance of the propulsion system: Real-time monitoring of the operation data of the propulsion system, protection of the propulsion system, fault monitoring and fault diagnosis;

[0072] The specific process of step S4 is as follows:

[0073] S41. Overload protection for permanent magnet propulsion motor: Real-time monitoring of the operating power of the permanent magnet propulsion motor. and rated power ,when When an overload is detected in the permanent magnet propulsion motor, the large model immediately activates the motor's protection function, cutting off the power supply within milliseconds to protect the permanent magnet propulsion motor and the entire propulsion system from damage; among which, Overload factor, The large model dynamically adjusts the overload coefficient based on the real-time operating status and historical data of the permanent magnet propulsion motor. ;

[0074] S42. Fault monitoring of the propulsion system: The propulsion system is based on a programmable logic controller (PLC) and transmits the status data of the electric propulsion equipment to a large model. When the monitored status data exceeds the normal range, it is determined that the electric propulsion equipment is operating abnormally and an alarm signal is immediately issued. The status data includes the current, voltage and speed of the propulsion motor.

[0075] S43. Fault diagnosis of propulsion system: Construct a diagnostic model based on fault data in historical data, pre-learn the motor's operating data under different fault states, and when the motor speed difference exceeds the threshold, the diagnostic model combines other motor parameters monitored in real time, matches and analyzes them in the knowledge base to obtain the motor's fault diagnosis result.

[0076] S5. Safety redundancy of power supply system: Monitor and regulate the power quality of DC power distribution system, and provide emergency power supply for AC power distribution system;

[0077] In step S5, the specific process of monitoring and regulating the power quality of the DC power distribution system is as follows:

[0078] S51. Real-time monitoring of the actual voltage of the DC bus in the DC power distribution system. Rated voltage and allowable voltage fluctuation range ;

[0079] S52, when At that time, the large model initiates the adjustment mechanism, using a voltage regulation algorithm based on proportional-integral-derivative control to control the DC bus voltage output. The calculation formula is as follows: ,in, DC bus voltage output; This is the proportionality coefficient; The integral time constant; The differential time constant; For voltage deviation, ; For DC output time; for The voltage value at that moment; For time;

[0080] S53, the large model dynamically adjusts itself by learning from historical data and analyzing real-time data. , , The value controls the power regulation device in the DC power distribution system to stabilize the DC bus voltage within the allowable range and improve the quality of the output power.

[0081] In step S5, the specific process of providing emergency power to the AC power distribution system is as follows:

[0082] S54. Monitor the operating status of the AC power distribution system in real time. When AC power fails, control the emergency power supply to provide power to the propulsion system and critical equipment; assuming the capacity of the emergency power supply is... The output voltage is The total power requirement for propulsion systems and key equipment is The duration for which emergency power supplies can provide continuous power. ;

[0083] The S55 emergency power supply is equipped with an intelligent charging device and control system. The large-scale model automatically adjusts the output power of the emergency power supply according to real-time changes in the ship's power demand; the charging and discharging efficiency of the emergency power supply is also taken into consideration. Actual available power Actual continuous power supply time .

[0084] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for energy efficiency management of electric tugboats based on a large model, characterized in that, Includes the following steps: S1. Dynamic Energy Allocation Optimization: Based on the real-time load of the electric tugboat, the dynamic energy allocation is optimized using a large model. S2. Charging strategy optimization: Optimize the charging time and charging amount of electric tugboats based on the price gradient of the power grid; The specific process of step S2 is as follows: S21, in slow charging mode, the large model combines the initial battery charge. Battery capacity and slow charging current The formula for predicting the time required to fully charge in slow charging mode is as follows: ,in, The time required to fully charge in slow charging mode; S22, in fast charging mode, the large model monitors the battery temperature in real time. ,Voltage and current Establish the charging current variation function By integrating the charging current change function, the time required for a full charge in fast charging mode is predicted. The calculation formula is as follows: ,in, The time required to fully charge in fast charging mode; S23. During the charging process, assume the electricity price during off-peak hours is... Electricity price during peak hours is , By combining electricity price information and charging data during peak and off-peak hours, the charging amount of electric tugboats is monitored in real time, with the charging amount during off-peak hours being... Peak charging volume is Calculate the total cost of charging the tugboat. ; S24. Based on a large model, considering the relationship between grid load and tugboat charging power, as well as electricity price differences, the tugboat charging power is dynamically adjusted through an optimization algorithm. Historical electric tugboat electricity demand data is analyzed to determine the optimal charging amount during off-peak hours. To minimize charging costs; S3. Battery system safety protection: Real-time monitoring of battery temperature, voltage and current, and early warning protection for the battery; S4. Safety assurance of the propulsion system: Real-time monitoring of the operation data of the propulsion system, protection of the propulsion system, fault monitoring and fault diagnosis; S5. Safety redundancy of power supply system: Monitor and regulate the power quality of DC power distribution system, and provide emergency power supply for AC power distribution system; In step S5, the specific process of monitoring and regulating the power quality of the DC power distribution system is as follows: S51. Real-time monitoring of the actual voltage of the DC bus in the DC power distribution system. Rated voltage and allowable voltage fluctuation range ; S52, when At that time, the large model initiates the adjustment mechanism, using a voltage regulation algorithm based on proportional-integral-derivative control to control the DC bus voltage output. The calculation formula is as follows: ,in, DC bus voltage output; This is the proportionality coefficient; The integral time constant; The differential time constant; For voltage deviation, ; For DC output time; for The voltage value at that moment; For time; S53, the large model dynamically adjusts itself by learning from historical data and analyzing real-time data. , , The value controls the power regulation device in the DC power distribution system to stabilize the DC bus voltage within the allowable range and improve the quality of the output power.

2. The energy efficiency management method for electric tugboats based on a large model as described in claim 1, characterized in that, The specific process of step S1 is as follows: S11. Based on a large model, predict and analyze the total power of electric tugboats under different operating scenarios. To increase motor power Power of other devices The sum of ; S12. The large model learns from historical operational data of the electric tugboat and analyzes in real time the optimal ratio of propulsion motor power to total power. , Historical operational data includes load weight, sailing speed, water current, and wind direction. S13, Propulsion motor power based on analysis of allocated power. Power allocated to other equipment Dynamic adjustment This value enables dynamic power allocation.

3. The energy efficiency management method for electric tugboats based on a large model as described in claim 1, characterized in that, Step S1 also includes battery pack charge-discharge balancing, the specific process of which is as follows: S14. The large model monitors the charging and discharging state of the battery pack in real time, assuming the initial state of charge of each battery pack is... ;in, , representing 8 groups of batteries; S15. During the discharge process, predict the discharge current of each battery group based on the real-time load. and discharge time 1. Considering the battery capacity Calculate the elapsed discharge time The state of charge of each battery group after step 1 is calculated using the following formula: ,in, The state of charge of the i-th battery group; S16. Execute the battery balancing algorithm program to adjust the discharge current of each battery group to keep the state of charge of each battery group consistent and reduce the accumulation of performance differences between battery groups; when it is found that the state of charge of a certain battery group is lower than the average value, the amount of reduction in the discharge current of this battery group is calculated based on the large model to achieve battery balancing management.

4. The energy efficiency management method for electric tugboats based on a large model as described in claim 1, characterized in that, The specific process of step S3 is as follows: S31. The battery temperature data is transmitted to the large model in real time via a temperature sensor. The large model analyzes the battery temperature data in real time. Let the critical temperature at which battery thermal runaway occurs be... When the battery temperature In case of thermal runaway, the blocking protection mechanism is immediately activated, cutting off the connection between batteries through smart fuses or electronic switches; during thermal runaway, the heat conduction equation is used. By combining real-time monitored battery temperature data, the risk of thermal runaway can be predicted in advance; among them, Density of battery materials; Specific heat capacity of battery materials; Thermal conductivity; The internal heat generation rate of the battery; S32. Collect and monitor the voltage and current of individual battery cells in real time. For overvoltage faults, assume the normal operating voltage range of the battery is... When the voltage of a single cell is monitored in real time satisfy When an overvoltage fault is detected, the connection between the batteries is disconnected via a smart fuse or electronic switch; for overcurrent faults, assuming the battery's rated current is... When the current of a single cell is monitored in real time satisfy When an overcurrent fault is detected in the battery, the connection between the batteries is cut off via a smart fuse or electronic switch. S33. Construct a Rint model to determine the battery's terminal voltage. With current Battery internal resistance and open circuit voltage The relationship is Utilizing the data analysis and learning capabilities of large models, the least squares method is used to analyze model parameters and determine battery health status and faults; among them, it is assumed that the collected data... Find the objective function based on the group terminal voltage and corresponding current data. smallest The value is calculated using the following formula: ,in, Let be the terminal voltage of the j-th battery group; Let be the current of the j-th battery group; right Seeking information about The derivative of , and set it to 0: Solving the equation yields an estimated value for the battery's internal resistance R.

5. The energy efficiency management method for electric tugboats based on a large model as described in claim 1, characterized in that, The specific process of step S4 is as follows: S41. Overload protection for permanent magnet propulsion motor: Real-time monitoring of the operating power of the permanent magnet propulsion motor. and rated power ,when When an overload is detected in the permanent magnet propulsion motor, the large model immediately activates the motor's protection function, cutting off the power supply within milliseconds to protect the permanent magnet propulsion motor and the entire propulsion system from damage; among which, Overload factor, The large model dynamically adjusts the overload coefficient based on the real-time operating status and historical data of the permanent magnet propulsion motor. ; S42. Fault monitoring of the propulsion system: The propulsion system is based on a programmable logic controller (PLC) and transmits the status data of the electric propulsion equipment to a large model. When the monitored status data exceeds the normal range, it is determined that the electric propulsion equipment is operating abnormally and an alarm signal is immediately issued. The status data includes the current, voltage and speed of the propulsion motor. S43. Fault diagnosis of propulsion system: Construct a diagnostic model based on fault data in historical data, pre-learn the motor's operating data under different fault states, and when the motor speed difference exceeds the threshold, the diagnostic model combines other motor parameters monitored in real time, matches and analyzes them in the knowledge base to obtain the motor fault diagnosis result.

6. The energy efficiency management method for electric tugboats based on a large model as described in claim 1, characterized in that, In step S5, the specific process of providing emergency power to the AC power distribution system is as follows: S54. Monitor the operating status of the AC power distribution system in real time. When AC power fails, control the emergency power supply to provide power to the propulsion system and critical equipment; assuming the capacity of the emergency power supply is... The output voltage is The total power requirement for propulsion systems and key equipment is The duration for which emergency power supplies can provide continuous power. ; The S55 emergency power supply is equipped with an intelligent charging device and control system. The large-scale model automatically adjusts the output power of the emergency power supply according to real-time changes in the ship's power demand; the charging and discharging efficiency of the emergency power supply is also taken into consideration. Actual available power Actual continuous power supply time .

Images