New energy ship long-endurance power control method and system

Abstract

The invention relates to the field of ship endurance control management, in particular to a new energy ship long endurance power control method and system, and the method specifically comprises the following steps: collecting the dynamic data of a ship power system and a water surface environment in real time; predicting battery attenuation and residual endurance according to the dynamic data; power distribution is dynamically adjusted according to the remaining endurance and battery attenuation, the endurance mileage of the ship is accurately predicted according to the water surface environment, then the remaining energy is properly distributed, and the endurance of the new energy ship is prolonged as much as possible.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of ship endurance control management, in particular to a new energy ship long endurance power control method and system. BACKGROUND

[0002] The new energy ship adopts electric power supply driving. In order to obtain the endurance mileage of the ship, real-time parameter collection of various aspects of the power system is needed, such as motor speed, voltage and current, etc., so as to calculate the current ship endurance.

[0003] For example, a pure electric ship electric power propulsion monitoring system with publication number CN208360452U, the CPU communicates with the frequency converter through the RS485 bus to collect the parameters of the related power system in the frequency converter in real time.

[0004] However, the battery is easily affected by temperature and load fluctuation during long-term navigation of the ship, resulting in irregular attenuation. These influences are related to the environmental conditions of the water area where the ship is located, such as wave height, wind speed and water temperature, etc. The irregular attenuation of the battery will cause energy waste and shorten the endurance. SUMMARY

[0005] In order to improve the endurance mileage of the new energy ship, the present application provides a new energy ship long endurance power control method and system.

[0006] In the first aspect, the new energy ship long endurance power control method provided by the present application adopts the following technical scheme.

[0007] A new energy ship long endurance power control method, specifically comprising the following steps.

[0008] S1, real-time collection of dynamic data of the ship power system and the water surface environment;

[0009] S2, predicting battery attenuation and remaining endurance according to the dynamic data;

[0010] S3, dynamically adjusting power distribution according to the remaining endurance and battery attenuation.

[0011] Optionally, in the S2, the dynamic data is input to the machine learning model every certain time to output the predicted SOH and the remaining endurance mileage.

[0012] Optionally, the predicted SOH is output via the battery electrochemical characteristics, thermodynamic characteristics, load dynamic characteristics, environmental erosion characteristics and SOC oscillation characteristics input to the LSTM neural network model.

[0013] By adopting the above technical solutions, combined with the real-time status of the battery packs of new energy ships and the water surface environment, more accurate predictions can be made of battery degradation and remaining range, so as to adapt and adjust the ship's propulsion power accordingly, reduce the possibility of abnormal battery degradation, and improve the ship's range.

[0014] Optionally, the formula for calculating the remaining driving range includes:

[0015] ;

[0016] in, This indicates the current percentage of remaining battery power on the ship. Indicates battery energy density, The average power consumption of the ship's propulsion system. It is an environmental loss factor.

[0017] Optionally, the The calculation formula includes:

[0018] ;

[0019] in, This is the wave loss coefficient. This is the drag coefficient. This is a water temperature correction factor. The wave height in the waters where the ship is located. This represents the wind speed at the ship's location. The water temperature of the waters where the ship is located, The preset optimal water temperature.

[0020] Optionally, the , and This method can measure the difference between the actual power loss and the theoretical reference power of a ship's propulsion system under simulated water conditions with different combinations of wave height, wind speed, and water temperature to form multiple sets of sample data. Least squares or multiple linear regression can then be used to analyze the data. The calculation formula is used to fit the data to minimize the fitting error. The calculation formula corresponds to , and The value is the optimal value.

[0021] Optionally, the The peak water temperature was determined by testing the product of the battery pack's charge / discharge efficiency and the motor's efficiency under standard load conditions at different water temperatures.

[0022] By adopting the above technical solution, the remaining driving range can be accurately calculated by combining parameters such as wave height, wind speed and water temperature of the waters where the ship is located.

[0023] Optionally, in step S3, if the remaining range is less than 80% of the target range, the system switches to low-power mode.

[0024] Optionally, the formula for optimizing the flight power based on battery degradation in S3 includes:

[0025] ;

[0026] in, The average power consumption of the ship's propulsion system. The predicted SOH value for the battery pack to complete its flight. This represents the current SOH value of the battery pack.

[0027] By adopting the above technical solutions, the power mode and specific power value of the ship's propulsion system can be adjusted accordingly, so that the ship's battery can maintain normal degradation, which helps to improve the ship's range.

[0028] Secondly, the technical solution adopted in this application for a long-endurance power control system for new energy ships is as follows.

[0029] A long-endurance power control system for new energy vessels, operating according to the aforementioned long-endurance power control method for new energy vessels, includes:

[0030] The data acquisition module is used to collect dynamic data on the ship's power system and the water surface environment in real time.

[0031] The feature transformation module converts dynamic data into high-dimensional feature vectors that can be parsed by the LSTM neural network model.

[0032] The predictive analysis module outputs predicted SOH and remaining driving range based on an LSTM neural network model.

[0033] The power optimization module dynamically adjusts power allocation based on remaining range and battery degradation.

[0034] By adopting the above technical solutions, the batteries of new energy ships can be better managed, making them less prone to abnormal degradation and helping to improve the ship's range.

[0035] In summary, this application includes at least the following beneficial effects:

[0036] By combining the real-time status of the battery packs of new energy ships with the water surface environment, more accurate predictions can be made of battery degradation and remaining range. This allows for adaptive adjustments to the ship's propulsion power, reducing the possibility of abnormal battery degradation and improving the ship's range. Attached Figure Description

[0037] Figure 1 This is a flowchart illustrating the steps of a long-endurance power control method for new energy ships. Detailed Implementation

[0038] The present application will be further described in detail below with reference to the accompanying drawings.

[0039] This application discloses a method for long-endurance power control of new energy ships, referring to... Figure 1 Specifically, it includes the following steps.

[0040] S1. Real-time acquisition of dynamic data on the ship's power system and the water surface environment.

[0041] The dynamic data of a ship's propulsion system mainly consists of battery pack data, such as battery temperature, current State of Charge (SOC), and State of Health (SOH). SOC represents the remaining battery charge, and SOH represents the battery's health status. This data is primarily acquired in real-time by the Battery Management System (BMS) in conjunction with thermistors. Dynamic data of the surface environment mainly includes water temperature, wind speed, and wave height, which are acquired by the ship using an Inertial Measurement Unit (IMU) and water temperature and wind speed sensors. Wave height is obtained by collecting motion parameters such as acceleration and angular velocity from the ship via the IMU, followed by filtering, Fourier transform, and amplitude conversion.

[0042] S2. Predicts battery degradation and remaining range based on dynamic data.

[0043] Dynamic data can be input into a machine learning model, such as an LSTM neural network model, every 5 minutes to output predicted SOH and remaining driving range.

[0044] The predicted SOH is output by inputting the battery electrochemical characteristics, thermodynamic characteristics, load dynamic characteristics, environmental erosion characteristics, and SOC oscillation characteristics into the LSTM neural network model.

[0045] Battery electrochemical characteristics refer to the imaginary part of the battery's AC internal resistance at a frequency of 1kHz, directly reflecting electrolyte aging. Ship vibrations accelerate electrolyte aging, i.e., battery degradation. Thermodynamic characteristics refer to the highest single-cell temperature of the battery pack; poor heat dissipation in the ship's engine room leads to a high risk of thermal runaway. Load dynamic characteristics refer to the effective current value within 5 minutes, reflecting the frequency of ship acceleration and deceleration. Environmental corrosion characteristics refer to the average wave height over 30 minutes; wave impact can cause mechanical stress fatigue in the battery pack. SOC oscillation characteristics refer to the fluctuation amplitude, frequency, and variation pattern of the battery's SOC value during the most recent charge-discharge cycle, used to determine the battery's SOH value.

[0046] The formula for calculating the remaining driving range is as follows.

[0047] ;

[0048] in, This indicates the current percentage of remaining battery power on the ship. This indicates the battery energy density, which can be obtained and preset from the battery datasheet. The average power consumption of the ship's propulsion system can be obtained by collecting the average motor power over the past 5 minutes from the frequency converter. The environmental loss factor reflects the additional power loss caused by the aquatic environment, typically ranging from 0 to 20 kW. The calculation formula is as follows.

[0049] ;

[0050] in, This is the wave loss coefficient. This is the drag coefficient. This is a water temperature correction factor. , and This method can measure the difference between the actual power loss and the theoretical reference power of a ship's propulsion system under simulated water conditions with different combinations of wave height, wind speed, and water temperature to form multiple sets of sample data. Least squares or multiple linear regression can then be used to analyze the data. The calculation formula is used to fit the data to minimize the fitting error. The calculation formula corresponds to , and The value is the optimal value.

[0051] The wave height in the waters where the ship is located. This represents the wind speed at the ship's location. The water temperature of the waters where the ship is located. The preset optimal water temperature, The peak water temperature was determined by testing the product of the battery pack's charge / discharge efficiency and the motor's efficiency under standard load conditions at different water temperatures.

[0052] S3 dynamically adjusts power allocation based on remaining range and battery degradation.

[0053] If the remaining range is less than 80% of the target range, the system will switch to low-power mode. Low-power mode can automatically reduce the power of non-critical loads, such as auxiliary lighting, or limit the motor torque, for example, the motor can only reach 80% of its normal torque.

[0054] The formula for optimizing flight power based on battery degradation is as follows.

[0055] ;

[0056] in, The average power consumption of the ship's propulsion system. The predicted SOH value for the battery pack to complete its flight. This represents the current SOH value of the battery pack.

[0057] This application also discloses a long-endurance power control system for new energy ships, including:

[0058] The data acquisition module is used to collect dynamic data on the ship's power system and the water surface environment in real time.

[0059] The feature transformation module converts dynamic data into high-dimensional feature vectors that can be parsed by the LSTM neural network model.

[0060] The predictive analysis module outputs predicted SOH and remaining driving range based on an LSTM neural network model.

[0061] The power optimization module dynamically adjusts power allocation based on remaining range and battery degradation.

[0062] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for controlling the power of new energy ships for long-range operation, characterized in that: Specifically, the following steps are included: S1. Real-time acquisition of dynamic data on the ship's power system and the water surface environment; S2. Predict battery degradation and remaining range based on dynamic data; S3 dynamically adjusts power allocation based on remaining range and battery degradation.

2. The method for long-endurance power control of new energy ships according to claim 1, characterized in that: In S2, dynamic data is input into the machine learning model at predetermined intervals to output the predicted SOH and remaining driving range.

3. The method for long-endurance power control of new energy ships according to claim 2, characterized in that: The predicted SOH is output via the battery electrochemical characteristics, thermodynamic characteristics, load dynamic characteristics, environmental erosion characteristics, and SOC oscillation characteristics input into the LSTM neural network model.

4. The method for long-endurance power control of new energy ships according to claim 2, characterized in that: The formula for calculating the remaining driving range includes: ； in, This indicates the current percentage of remaining battery power on the ship. Indicates battery energy density, The average power consumption of the ship's propulsion system. It is an environmental loss factor.

5. The method for long-endurance power control of new energy ships according to claim 4, characterized in that: The The calculation formula includes: ； in, This is the wave loss coefficient. This is the drag coefficient. This is a water temperature correction factor. The wave height in the waters where the ship is located. This represents the wind speed at the ship's location. The water temperature of the waters where the ship is located, The preset optimal water temperature.

6. The method for long-endurance power control of new energy ships according to claim 5, characterized in that: The , and This method can measure the difference between the actual power loss and the theoretical reference power of a ship's propulsion system under simulated water conditions with different combinations of wave height, wind speed, and water temperature to form multiple sets of sample data. Least squares or multiple linear regression can then be used to analyze the data. The calculation formula is used to fit the data to minimize the fitting error. The calculation formula corresponds to , and The value is the optimal value.

7. The method for long-endurance power control of new energy ships according to claim 5, characterized in that: The The peak water temperature was determined by testing the product of the battery pack's charge / discharge efficiency and the motor's efficiency under standard load conditions at different water temperatures.

8. The method for long-endurance power control of new energy ships according to claim 1, characterized in that: If the remaining range in S3 is less than 80% of the target range, then switch to low power mode.

9. The method for long-endurance power control of new energy ships according to claim 1, characterized in that: The formula for optimizing the flight power based on battery degradation in S3 includes: ； in, The average power consumption of the ship's propulsion system. The predicted SOH value for the battery pack to complete its flight. This represents the current SOH value of the battery pack.

10. A long-endurance power control system for new energy ships, operating according to the long-endurance power control method for new energy ships as described in any one of claims 1-9, characterized in that: include: The data acquisition module is used to collect dynamic data on the ship's power system and the water surface environment in real time. The feature transformation module converts dynamic data into high-dimensional feature vectors that can be parsed by the LSTM neural network model. The predictive analysis module outputs predicted SOH and remaining driving range based on an LSTM neural network model. The power optimization module dynamically adjusts power allocation based on remaining range and battery degradation.

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