CN107274085B - Optimal management method for energy storage equipment of double-electric ship - Google Patents
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Abstract
The invention provides an optimal management method of energy storage equipment of a double-electric ship, which is used for obtaining the power and energy requirements of the ship according to the typical working cycle of a target ship; collecting specification parameters of lithium iron phosphate batteries, supercapacitors and propulsion motors of various manufacturers; establishing an electric propulsion system model of the whole target ship by taking the ship energy efficiency index and the price of the energy storage equipment as targets; performing multi-objective optimization calculation by adopting a genetic algorithm to obtain an optimal energy storage equipment model selection scheme; establishing an energy demand prediction model for the energy storage equipment after the type selection, and establishing a rolling optimization strategy on a limited period of time; establishing a fuzzy controller, establishing a control rule base of the ship energy management system particle swarm fuzzy controller, and optimizing the fuzzy controller by using an intelligent swarm theory, namely a particle swarm optimization method. According to the invention, the capacity of each energy storage device is reasonably selected and the flow direction of energy is intelligently controlled, so that the economic performance of the ship is improved and the service life of the battery is prolonged.
Description
Technical Field
The invention relates to the field of pure electric ship energy storage devices, in particular to an optimal management method for energy storage equipment of a double-electric ship.
Background
In recent years, various energy storage devices such as super capacitors and storage batteries are developed rapidly, and the performance is greatly improved. The application of the method has related cases for ships. The energy storage characteristics of various energy storage devices are different due to the difference of the structures and principles, for example, the super capacitor discharges quickly but has low power density; the lithium iron phosphate battery has high power density, but has limited capacity of bearing discharge current, and makes up for deficiencies according to characteristics of the lithium iron phosphate battery to form a composite energy storage device, namely a double-battery type, so that the lithium iron phosphate battery can better meet the power requirement of ships and can prolong the service life of the energy storage device. However, better energy management and optimization are needed for the energy storage device and how to reasonably use the energy storage device, which is important for exerting the characteristics of the energy storage device to the maximum extent, prolonging the service life of the energy storage device and reducing maintenance.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the optimal management method for the energy storage equipment of the double-electric ship is provided, the energy storage equipment can be reasonably used, and the service life of the energy storage equipment is prolonged.
The technical scheme adopted by the invention for solving the technical problems is as follows: an optimal management method for energy storage equipment of a double-electric ship is characterized by comprising the following steps: it includes:
the method comprises the following steps: energy storage equipment type selection:
data acquisition: obtaining the power and energy requirements of the ship according to the typical work cycle of the target ship; collecting specification parameters of lithium iron phosphate batteries, supercapacitors and propulsion motors of various manufacturers to manufacture a traction table, and obtaining power equipment model selection of a target ship from the traction table;
establishing a model: establishing an electric propulsion system model of the whole target ship by taking the ship energy efficiency index and the price of the energy storage equipment as targets; the whole ship electric propulsion model comprises a converted oil consumption model, a motor model and a battery energy management model;
calculating and selecting types: the power and energy requirements are led into an electric propulsion system model, multi-objective optimization calculation is carried out by adopting a genetic algorithm to obtain a group of pareto optimal solutions, and the optimal energy storage equipment model selection scheme is obtained corresponding to actual specific equipment in a traction table;
step two, energy management:
establishing an energy demand prediction model for the energy storage equipment after the type selection, and predicting the power demand of the ship in the next period according to the historical navigation data of the ship and the real-time information provided by the intelligent transportation system; establishing a rolling optimization strategy in a limited time period, correcting the prediction of the ship model according to the actual power requirement of the ship at the next sampling moment, and then performing new optimization prediction;
establishing a fuzzy controller, fuzzifying the charge state, the power demand and the predicted power demand of the energy storage equipment to form input fuzzy variables, and then transmitting each input fuzzy variable to the fuzzy controller; and establishing a control rule base of the ship energy management system particle swarm fuzzy controller, and optimizing the fuzzy controller by using an intelligent swarm theory, namely a particle swarm optimization method.
According to the method, the power and energy requirements are a group of data related to time obtained according to the long-time working statistics of the current working ship of the same type as the target ship.
According to the method, the specification parameters of the lithium iron phosphate battery and the super capacitor comprise battery capacity, battery monomer weight, battery monomer price and charging and discharging curves; the specification parameters of the propulsion motor comprise total installation weight and an efficiency characteristic curve.
According to the method, the genetic algorithm comprises the following calculation steps:
1) defining two variables X1 and X2, and carrying out real number encoding on the two variables;
2) setting a population scale, and generating an initial population according to constraint conditions;
3) performing rapid non-dominated sorting and virtual crowding distance calculation on the contemporary population; the fast non-dominated sorting is carried out according to two objective function values of each type-selected ship energy efficiency design index and the total price of the energy storage device, and the virtual crowding degree distance is obtained according to the distance information of the individual vector in the variable space;
4) determining an energy efficiency design index EEDI and a total Price of energy storage equipment of the ship as calculated optimization targets, wherein the mathematical expression is as follows:
Price＝Mb*n1+Ms*n2
in the formula: s is the conversion coefficient of carbon dioxide, P is the power of the power system, f is the correction coefficient, fiFor considering the maximum design loading condition of the ship due to technical or regulatory requirementsA limited dimensionless correction coefficient, wherein Capacity is the total tonnage of the ship; vrefUnder the maximum design loading condition, under the condition of propulsion by the defined shaft power, the ship speed is under the calm sea condition without wind and wave; f. ofwThe method is a dimensionless coefficient for considering the influence of wave height, wave frequency and wind speed on the ship speed; mbFor the price of the battery cell, MsIs the price of the capacitor cell, n1Is the number of cells, n2The number of the capacitor monomers;
5) performing genetic manipulations, including selection, crossover and mutation; setting selection probability, recombination rate and mutation rate to obtain a sub-population;
6) performing an elite retention strategy, namely combining the parent population and the child population, selecting based on rapid non-dominated sorting and virtual crowding distance, and then, parameter-selecting the next-generation parent population; the number of iterations is increased by 1 and returns to 3) until the number of iterations reaches the set maximum value.
According to the method, the battery energy management model is divided into the following working modes:
(1) when the power requirement is greater than the upper threshold value and the target ship works at starting, rapid acceleration or high load, the super capacitor bank and the lithium iron phosphate battery pack work together to provide energy for the motor;
(2) when the power requirement is between an upper threshold and a lower threshold and the target ship works in an acceleration state, the super capacitor bank preferentially discharges large current quickly to provide acceleration energy for the propulsion motor;
(3) when the power demand is smaller than the lower threshold value and the target ship works in a stable sailing state, the lithium iron phosphate battery pack preferentially works to provide energy for the propulsion motor.
According to the method, the battery energy management model controls the discharge current of the energy storage device according to the power requirement of the target ship and the charge state of the battery.
The invention has the beneficial effects that: starting from two aspects of type selection and energy management of the energy storage devices, on the premise of ensuring the dynamic performance of the ship, the economic performance of the ship is improved and the service life of a battery is prolonged by reasonably selecting the capacity of each energy storage device and intelligently controlling the flow direction of energy.
Drawings
FIG. 1 is a flowchart of a method according to an embodiment of the present invention.
Detailed Description
The invention is further illustrated by the following specific examples and figures.
The invention provides an optimization management method of energy storage equipment of a double-electric ship, which comprises the following steps as shown in figure 1:
the method comprises the following steps: energy storage equipment type selection:
data acquisition: obtaining the power and energy requirements of the ship according to the typical work cycle of the target ship; and collecting specification parameters of lithium iron phosphate batteries, supercapacitors and propulsion motors of various manufacturers to manufacture a traction table, and obtaining the power equipment model selection of the target ship from the traction table. The power and energy requirements are a group of data related to time obtained according to long-time working statistics of the current working ship of the same type as the target ship. The specification parameters of the lithium iron phosphate battery and the super capacitor comprise battery capacity, battery monomer weight, battery monomer price and a charging and discharging curve; the specification parameters of the propulsion motor comprise total installation weight and an efficiency characteristic curve.
Establishing a model: establishing an electric propulsion system model of the whole target ship by taking the ship energy efficiency index and the price of the energy storage equipment as targets; the whole ship electric propulsion model comprises a converted oil consumption model, a motor model and a battery energy management model.
And the battery energy management model controls the discharge current of the energy storage device according to the power requirement of the target ship and the charge state of the battery. In the battery energy management model, the working modes are divided as follows:
(1) when the power requirement is greater than the upper threshold value and the target ship works at starting, rapid acceleration or high load, the super capacitor bank and the lithium iron phosphate battery pack work together to provide energy for the motor;
(2) when the power requirement is between an upper threshold and a lower threshold and the target ship works in an acceleration state, the super capacitor bank preferentially discharges large current quickly to provide acceleration energy for the propulsion motor;
(3) when the power demand is smaller than the lower threshold value and the target ship works in a stable sailing state, the lithium iron phosphate battery pack preferentially works to provide energy for the propulsion motor.
Calculating and selecting types: and importing the power and energy requirements into an electric propulsion system model, performing multi-objective optimization calculation by adopting a genetic algorithm to obtain a group of pareto optimal solutions, and obtaining an optimal energy storage equipment model selection scheme corresponding to actual specific equipment in a traction table.
The genetic algorithm comprises the following calculation steps:
1) two variables X1, X2 are defined, which are real number encoded.
2) And setting the population scale, and generating an initial population according to the constraint condition.
3) Performing rapid non-dominated sorting and virtual crowding distance calculation on the contemporary population; the fast non-dominated sorting is carried out according to two objective function values of each type-selected ship energy efficiency design index and the total price of the energy storage device, and the virtual crowding degree distance is obtained according to distance information of the individual vector in the variable space.
4) Determining an energy efficiency design index EEDI and a total Price of energy storage equipment of the ship as calculated optimization targets, wherein the mathematical expression is as follows:
Price＝Mb*n1+Ms*n2
in the formula: s is the conversion coefficient of carbon dioxide, P is the power of the power system, f is the correction coefficient, fiIn order to consider a dimensionless correction coefficient which limits the maximum design loading condition of the ship due to technical or specified requirements, Capacity is the total tonnage of the ship; vrefUnder the maximum design loading condition, under the condition of propulsion by the defined shaft power, the ship speed is under the calm sea condition without wind and wave; f. ofwTo take the wave height into consideration,Dimensionless coefficients of the influence of the wave frequency and the wind speed on the ship speed; mbFor the price of the battery cell, MsIs the price of the capacitor cell, n1Is the number of cells, n2The number of the capacitor monomers is shown.
5) Performing genetic manipulations, including selection, crossover and mutation; setting selection probability, recombination rate and mutation rate to obtain a sub-population; genetic manipulation is a core link of optimization iteration of NSGA-II, wherein the selection manipulation is based on 3).
6) Performing an elite retention strategy, namely combining the parent population and the child population, selecting based on rapid non-dominated sorting and virtual crowding distance, and then, parameter-selecting the next-generation parent population; the number of iterations is increased by 1 and returns to 3) until the number of iterations reaches the set maximum value.
Step two, energy management:
establishing an energy demand prediction model for the energy storage equipment after the type selection, and predicting the power demand of the ship in the next period according to the historical navigation data of the ship and the real-time information provided by the intelligent transportation system; and establishing a rolling optimization strategy in a limited time period, avoiding uncertainty caused by model mismatch, time variation and interference under complex working conditions, correcting the prediction of the ship model according to the actual power requirement of the ship at the next sampling moment, and then performing new optimization prediction.
Establishing a fuzzy controller, fuzzifying the charge state, the power demand and the predicted power demand of the energy storage equipment to form input fuzzy variables, and then transmitting each input fuzzy variable to the fuzzy controller; and establishing a control rule base of the ship energy management system particle swarm fuzzy controller, and optimizing the fuzzy controller by using an intelligent swarm theory, namely a particle swarm optimization method.
The invention relates to an optimized management method for energy storage equipment of a 'double-electric type' pure electric ship with 'lithium iron phosphate battery + super capacitor', aiming at improving the economic performance of the ship and prolonging the service life of a battery by reasonably selecting the capacity of each energy storage device and intelligently controlling the flow direction of energy on the premise of ensuring the dynamic performance of the ship.
The energy storage device type selection method comprises the following steps: obtaining the power and energy requirements of the ship according to the typical work cycle of the target ship; building an electric propulsion system model of a target ship; and importing the collected power demand data of the reference ship into a model, adopting an NSGA-II algorithm control strategy based on a multi-objective genetic algorithm, taking EEDI (ship energy efficiency design index) and price of the ship as optimization targets, performing genetic algorithm calculation to obtain a group of pareto optimal solutions, and obtaining a proper type selection scheme corresponding to actual specific equipment.
The method adopts an energy management fuzzy control strategy based on model prediction to divide the composite power supply into 3 working modes, which are respectively as follows: the system comprises a super capacitor bank independent working mode, a super capacitor bank and lithium iron phosphate battery pack common working mode and a lithium iron phosphate battery pack independent working mode. The power demand of the ship in the next time period is predicted according to the power demand, the charge state and the real-time running state of the energy storage equipment under different working conditions and the real-time information provided by historical navigation data, a mathematical model and an intelligent traffic system, on the basis, the fuzzy controller based on the particle swarm optimization algorithm is adopted to reasonably control the working mode of the composite power supply, so that the energy distribution and the recovery of the energy storage devices are realized, each energy storage device can exert the advantages, the economic performance of the ship is improved, and the service life of a battery is prolonged.
The above embodiments are only used for illustrating the design idea and features of the present invention, and the purpose of the present invention is to enable those skilled in the art to understand the content of the present invention and implement the present invention accordingly, and the protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes and modifications made in accordance with the principles and concepts disclosed herein are intended to be included within the scope of the present invention.
Claims (1)
1. An optimal management method for energy storage equipment of a double-electric ship is characterized by comprising the following steps: it includes:
the method comprises the following steps: energy storage equipment type selection:
data acquisition: obtaining the power and energy requirements of the ship according to the typical work cycle of the target ship; collecting specification parameters of lithium iron phosphate batteries, supercapacitors and propulsion motors of various manufacturers to manufacture a traction table, and obtaining power equipment model selection of a target ship from the traction table;
establishing a model: establishing an electric propulsion system model of the whole target ship by taking the ship energy efficiency index and the price of the energy storage equipment as targets; the whole ship electric propulsion model comprises a converted oil consumption model, a motor model and a battery energy management model;
calculating and selecting types: the power and energy requirements are led into an electric propulsion system model, multi-objective optimization calculation is carried out by adopting a genetic algorithm to obtain a group of pareto optimal solutions, and the optimal energy storage equipment model selection scheme is obtained corresponding to actual specific equipment in a traction table;
step two, energy management:
establishing an energy demand prediction model for the energy storage equipment after the type selection, and predicting the power demand of the ship in the next period according to the historical navigation data of the ship and the real-time information provided by the intelligent transportation system; establishing a rolling optimization strategy in a limited time period, correcting the prediction of the ship model according to the actual power requirement of the ship at the next sampling moment, and then performing new optimization prediction;
establishing a fuzzy controller, fuzzifying the charge state, the power demand and the predicted power demand of the energy storage equipment to form input fuzzy variables, and then transmitting each input fuzzy variable to the fuzzy controller; establishing a control rule base of a ship energy management system particle swarm fuzzy controller, and optimizing the fuzzy controller by using an intelligent swarm theory, namely a particle swarm optimization method;
the power and energy requirements are a group of data related to time obtained by long-time working statistics of the current working ship of the same type as the target ship;
the specification parameters of the lithium iron phosphate battery and the super capacitor comprise battery capacity, battery monomer weight, battery monomer price and a charging and discharging curve; the specification parameters of the propulsion motor comprise total installation weight and an efficiency characteristic curve;
the genetic algorithm comprises the following calculation steps:
1) defining two variables X1 and X2, and carrying out real number encoding on the two variables;
2) setting a population scale, and generating an initial population according to constraint conditions;
3) performing rapid non-dominated sorting and virtual crowding distance calculation on the contemporary population; the fast non-dominated sorting is carried out according to two objective function values of each type-selected ship energy efficiency design index and the total price of the energy storage device, and the virtual crowding degree distance is obtained according to the distance information of the individual vector in the variable space;
4) determining an energy efficiency design index EEDI and a total Price of energy storage equipment of the ship as calculated optimization targets, wherein the mathematical expression is as follows:
Price＝Mb*n1+Ms*n2
in the formula: s is the conversion coefficient of carbon dioxide, P is the power of the power system, f is the correction coefficient, fiIn order to consider a dimensionless correction coefficient which limits the maximum design loading condition of the ship due to technical or specified requirements, Capacity is the total tonnage of the ship; vrefUnder the maximum design loading condition, under the condition of propulsion by the defined shaft power, the ship speed is under the calm sea condition without wind and wave; f. ofwThe method is a dimensionless coefficient for considering the influence of wave height, wave frequency and wind speed on the ship speed; mbFor the price of the battery cell, MsIs the price of the capacitor cell, n1Is the number of cells, n2The number of the capacitor monomers;
5) performing genetic manipulations, including selection, crossover and mutation; setting selection probability, recombination rate and mutation rate to obtain a sub-population;
6) performing an elite retention strategy, namely combining the parent population and the child population, selecting based on rapid non-dominated sorting and virtual crowding distance, and then, parameter-selecting the next-generation parent population; adding 1 to the iteration times, and returning to 3) until the iteration times reach the set maximum value;
in the battery energy management model, the working modes are divided as follows:
(1) when the power requirement is greater than the upper threshold value and the target ship works at starting, rapid acceleration or high load, the super capacitor bank and the lithium iron phosphate battery pack work together to provide energy for the motor;
(2) when the power requirement is between an upper threshold and a lower threshold and the target ship works in an acceleration state, the super capacitor bank preferentially discharges large current quickly to provide acceleration energy for the propulsion motor;
(3) when the power requirement is smaller than a lower threshold value and the target ship works in a stable sailing state, the lithium iron phosphate battery pack preferentially works to provide energy for the propulsion motor;
the battery energy management model controls the discharging current of the energy storage device according to the power requirement of the target ship and the charge state of the battery.
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| CN110147583A (en) * | 2019-04-23 | 2019-08-20 | 常伟 | It is a kind of that the integrated application analysis method of energy-storage system is carried out by chemical energy storage battery |
| CN112217190B (en) * | 2019-07-10 | 2022-11-25 | 株洲中车时代电气股份有限公司 | Hybrid power system for ship and control method thereof |
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| CN113255250B (en) * | 2021-07-02 | 2021-09-17 | 湖北东湖实验室 | Accurate design method for battery capacity of battery-powered ship |
| CN116362296B (en) * | 2022-10-31 | 2024-03-01 | 无锡赛思亿电气科技有限公司 | Ship energy efficiency information collection management system and energy consumption state analysis method based on same |
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