CN107274085A - A kind of optimum management method of the energy storage device of double electric type ships - Google Patents

Abstract

The invention provides an optimized management method for the energy storage equipment of a dual-electric ship, which obtains the power and energy requirements of the ship according to the typical working cycle of the target ship; collects the specification parameters of lithium iron phosphate batteries, supercapacitors, and propulsion motors of various manufacturers; Taking the energy efficiency index of the ship and the price of energy storage equipment as the target, build the electric propulsion system model of the target ship; use the genetic algorithm for multi-objective optimization calculation to obtain the best energy storage equipment selection scheme; The energy demand prediction model is established for energy equipment, and the rolling optimization strategy based on a limited period of time is established; the fuzzy controller is established, and the control rule library of the particle swarm fuzzy controller for the ship energy management system is established, and the intelligent swarm theory, that is, the particle swarm optimization method is used to control the fuzzy The controller is optimized. The invention improves the economic performance of the ship and prolongs the service life of the battery by rationally selecting the capacity of each energy storage device and intelligently controlling the energy flow.

Description

An optimized management method for energy storage equipment of a dual-electric ship

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 devices of dual-electric ships.

Background technique

In recent years, various energy storage devices such as supercapacitors and batteries have developed rapidly, and their performance has been greatly improved. There have been relevant cases of its application in ships. Various energy storage devices have different energy storage characteristics due to their differences in structure and principle. For example, supercapacitors discharge quickly, but have low power density; lithium iron phosphate batteries have high power density, but their ability to withstand discharge current is limited. Based on their characteristics, learn from each other. Constitute a composite energy storage device, that is, a dual-electric type, so that it can better respond to the power demand of the ship, and can also extend the life of the energy storage device. However, better energy management and optimization of the two are required, and how to use the energy storage device reasonably plays an important role in maximizing the characteristics of the energy storage device, prolonging its service life, and reducing maintenance.

Contents of the invention

The technical problem to be solved by the present invention is to provide an optimized management method for the energy storage equipment of a dual-electric ship, which can reasonably use the energy storage equipment and prolong the service life of the energy storage equipment.

The technical solution adopted by the present invention to solve the above technical problems is: an optimal management method for energy storage equipment of a dual-electric ship, which is characterized in that it includes:

Step 1: Selection of energy storage equipment:

Data collection: According to the typical working cycle of the target ship, the power and energy requirements of the ship are obtained; the specifications and parameters of lithium iron phosphate batteries, super capacitors, and propulsion motors of various manufacturers are collected to make a traction table, and the target ship’s power is obtained from the traction table. power equipment selection;

Model establishment: with the target of ship energy efficiency index and energy storage equipment price, build the electric propulsion system model of the whole ship of the target ship; the electric propulsion model of the whole ship includes conversion fuel consumption model, motor model and battery energy management model;

Calculation and selection: import the power and energy requirements into the electric propulsion system model, use the genetic algorithm to perform multi-objective optimization calculations, and obtain a set of Pareto optimal solutions, corresponding to the actual specific equipment in the traction table, to obtain the best Energy storage equipment selection scheme;

Step 2. Energy management:

For the energy storage equipment that has been selected, establish an energy demand prediction model, and predict the power demand of the ship in the next period based on the historical navigation data of the ship and the real-time information provided by the intelligent transportation system; establish a rolling optimization strategy based on a limited period of time, At the next sampling time, according to the actual power demand of the ship, the prediction of the model of the ship is corrected, and then a new optimized prediction is made;

Establish a fuzzy controller to fuzzify the state of charge, power demand, and predicted power demand of the energy storage device to form input fuzzy variables, and then transmit each input fuzzy variable to the fuzzy controller; establish a particle swarm fuzzy control system for the ship energy management system The control rule library of the controller uses the intelligent group theory, that is, the particle swarm optimization method to optimize the fuzzy controller.

According to the above method, the power and energy requirements are a group of time-related data obtained from statistics of working ships of the same type as the target ship for a long time.

According to the above method, the specification parameters of the lithium iron phosphate battery and supercapacitor include battery capacity, battery cell weight, battery cell price and charge-discharge curve; the specification parameters of the propulsion motor include total installation weight, efficiency characteristics curve.

According to the above method, the calculation steps of the genetic algorithm include:

1) Define two variables X1 and X2, and encode these two variables with real numbers;

2) Set the population size and generate the initial population according to the constraints;

3) Carry out fast non-dominated sorting and calculation of virtual congestion distance for the contemporary population; among them, the fast non-dominated sorting is carried out according to the two objective function values of the energy efficiency design index of each selected ship and the total price of the energy storage device, The virtual crowding distance is obtained according to the distance information of the individual vector in the variable space;

4) The energy efficiency design index EEDI of the ship and the total price of energy storage equipment Price are determined as the optimization targets for calculation, and their mathematical expressions are as follows:

Price＝M _b *n ₁ +M _s *n ₂

In the formula: S is the conversion factor of carbon dioxide, P is the power of the power system, f is the correction factor, f _i is the dimensionless correction factor considering the limit of the maximum design loading condition of the ship due to technical or regulatory requirements, and Capacity is Gross tonnage of the ship; V _ref is the speed of the ship in calm sea conditions with no wind and waves under the condition of maximum design loading condition and propelled by the defined shaft power; f _w is considering wave height, wave frequency and wind speed Dimensionless coefficient of influence on ship speed; M _b is the price of battery cells, M _s is the price of capacitor cells, n ₁ is the number of battery cells, n ₂ is the number of capacitor cells;

5) Carry out genetic operations, including selection, crossover and mutation; set selection probability, recombination rate and mutation rate to obtain subpopulations;

6) Carry out the elite retention strategy, that is, merge the parent population with the child population, and perform selection based on fast non-dominated sorting and virtual crowding distance, and then parameterize the next generation parent population; increase the number of iterations by 1, and return to 3), Until the number of iterations reaches the set maximum value.

According to the above method, in the battery energy management model, the working modes are divided as follows:

(1) When the power demand is greater than the upper threshold, and the target ship is at the start, 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 demand is between the upper and lower thresholds and the target ship is in an accelerated state, the supercapacitor bank will give priority to large current and rapid discharge to provide acceleration energy for the propulsion motor;

(3) When the power demand is less than the lower threshold and the target ship is working in a stable sailing state, the lithium iron phosphate battery pack will work first to provide energy for the propulsion motor.

According to the above method, the battery energy management model controls the discharge current of the energy storage device according to the power demand of the target ship combined with the state of charge of the battery.

The beneficial effects of the present invention are as follows: starting from the selection of energy storage equipment and energy management, on the premise of ensuring the dynamic performance of the ship, by reasonably selecting the capacity of each energy storage equipment and intelligently controlling the flow of energy, improving economical performance of the vessel and prolong battery life.

Description of drawings

FIG. 1 is a flowchart of a method according to an embodiment of the present invention.

detailed description

The present invention will be further described below in conjunction with specific examples and accompanying drawings.

The present invention provides an optimized management method for energy storage equipment of a dual-electric ship, as shown in Figure 1, which includes:

Step 1: Selection of energy storage equipment:

Data collection: According to the typical working cycle of the target ship, the power and energy requirements of the ship are obtained; the specifications and parameters of lithium iron phosphate batteries, super capacitors, and propulsion motors of various manufacturers are collected to make a traction table, and the target ship’s power is obtained from the traction table. Power equipment selection. The power and energy requirements mentioned above are a set of time-related data obtained according to the long-time working statistics of working ships of the same type as the target ship. The specification parameters of the lithium iron phosphate battery and supercapacitor include battery capacity, battery cell weight, battery cell price and charge-discharge curve; the specification parameters of the propulsion motor include total installation weight and efficiency characteristic curve.

Model establishment: with the target of ship energy efficiency index and energy storage equipment price, build the electric propulsion system model of the target ship; the electric propulsion model of the whole ship includes conversion fuel consumption model, motor model and battery energy management model.

The battery energy management model controls the discharge current of the energy storage device according to the power demand of the target ship combined with the state of charge of the battery. In the described battery energy management model, the working modes are divided as follows:

(1) When the power demand is greater than the upper threshold, and the target ship is at the start, 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 demand is between the upper and lower thresholds and the target ship is in an accelerated state, the supercapacitor bank will give priority to large current and rapid discharge to provide acceleration energy for the propulsion motor;

(3) When the power demand is less than the lower threshold and the target ship is working in a stable sailing state, the lithium iron phosphate battery pack will work first to provide energy for the propulsion motor.

Calculation and selection: import the power and energy requirements into the electric propulsion system model, use the genetic algorithm to perform multi-objective optimization calculations, and obtain a set of Pareto optimal solutions, corresponding to the actual specific equipment in the traction table, to obtain the best Energy storage equipment selection scheme.

The calculation steps of the genetic algorithm include:

1) Define two variables X1 and X2, and encode these two variables with real numbers.

2) Set the population size and generate the initial population according to the constraints.

3) Carry out fast non-dominated sorting and calculation of virtual congestion distance for the contemporary population; among them, the fast non-dominated sorting is carried out according to the two objective function values of the energy efficiency design index of each selected ship and the total price of the energy storage device, The virtual crowding distance is obtained according to the distance information of the individual vectors in the variable space.

4) The energy efficiency design index EEDI of the ship and the total price of energy storage equipment Price are determined as the optimization targets for calculation, and their mathematical expressions are as follows:

Price＝M _b *n ₁ +M _s *n ₂

In the formula: S is the conversion factor of carbon dioxide, P is the power of the power system, f is the correction factor, f _i is the dimensionless correction factor considering the limit of the maximum design loading condition of the ship due to technical or regulatory requirements, and Capacity is Gross tonnage of the ship; V _ref is the speed of the ship in calm sea conditions with no wind and waves under the condition of maximum design loading condition and propelled by the defined shaft power; f _w is considering wave height, wave frequency and wind speed Dimensionless coefficient of influence on ship speed; M _b is the price of battery cells, M _s is the price of capacitor cells, n ₁ is the number of battery cells, and n ₂ is the number of capacitor cells.

5) Perform genetic operations, including selection, crossover, and mutation; set selection probability, recombination rate, and mutation rate to obtain subpopulations; genetic operations are the core link of NSGA-II's optimization iteration, and the selection operation is based on 3) .

6) Carry out the elite retention strategy, that is, merge the parent population with the child population, and perform selection based on fast non-dominated sorting and virtual crowding distance, and then parameterize the next generation parent population; increase the number of iterations by 1, and return to 3), Until the number of iterations reaches the set maximum value.

Step 2. Energy management:

For the energy storage equipment that has been selected, establish an energy demand prediction model, and predict the power demand of the ship in the next period based on the historical navigation data of the ship and the real-time information provided by the intelligent transportation system; establish a rolling optimization strategy based on a limited period of time, To avoid uncertainty caused by model mismatch, time-varying, and interference in complex working conditions, at the next sampling time, according to the actual power demand of the ship, the model prediction of the ship is corrected, and then a new optimization is performed predict.

Establish a fuzzy controller to fuzzify the state of charge, power demand, and predicted power demand of the energy storage device to form input fuzzy variables, and then transmit each input fuzzy variable to the fuzzy controller; establish a particle swarm fuzzy control system for the ship energy management system The control rule library of the controller uses the intelligent group theory, that is, the particle swarm optimization method to optimize the fuzzy controller.

The present invention relates to a method for optimizing and managing the energy storage equipment of a "dual-electric" pure electric ship of "lithium iron phosphate battery + super capacitor". The capacity of each energy storage device and the intelligent control of energy flow can improve the economic performance of the ship and prolong the service life of the battery.

The selection of energy storage equipment includes the following steps: According to the typical working cycle of the target ship, the power and energy requirements of the ship are obtained; the electric propulsion system model of the target ship is built; the power demand data of the reference ship collected is imported into the model. The NSGA-Ⅱ algorithm control strategy in the multi-objective genetic algorithm takes the ship's EEDI (Ship Energy Efficiency Design Index) and price as the optimization objectives, performs genetic algorithm calculations, and obtains a set of Pareto optimal solutions, corresponding to the actual specific equipment. Appropriate selection scheme.

Using the energy management fuzzy control strategy based on model prediction, the composite power supply is divided into three working modes, namely: supercapacitor bank alone working mode, supercapacitor bank and lithium iron phosphate battery pack working together, and lithium iron phosphate battery working alone . According to the power demand under different working conditions, the state of charge of the energy storage device and the real-time operation status, according to the historical navigation data, mathematical models and real-time information provided by the intelligent transportation system, the power demand of the ship in the next period is predicted. Based on this A fuzzy controller based on the particle swarm optimization algorithm is used to reasonably control the working mode of the composite power supply to realize the energy distribution and recovery of the energy storage device, so that each energy storage device can play its advantages and improve the economic performance of the ship. Extend battery life.

The above embodiments are only used to illustrate the design concept and characteristics of the present invention, and its purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims (6)

1. a kind of optimum management method of the energy storage device of double electric type ships, it is characterised in that：It includes：

Step one：Energy storage device type selecting：

Data acquisition：The power and energy requirement for obtaining ship are circulated according to the exemplary operation of target ship；Collect each manufacturer's phosphorus Sour lithium iron battery, ultracapacitor, the specifications parameter of propulsion electric machine, are made traction table, and object ship is obtained from the traction table The power-equipment type selecting of oceangoing ship；

Model is set up：Using ship energy efficiency index and energy storage device price as target, the electric propulsion system of target ship whole ship is built System model；The whole ship electric propulsion model includes conversion oil consumption model, motor model and battery energy management model；

Lectotype and calculation：Described power and energy requirement are imported in electric propulsion system model, carried out using genetic algorithm many Objective optimization is calculated, and is obtained the actual specific equipment in one group of Pareto optimal solution, correspondence traction table, is obtained optimal energy storage and set Standby selecting type scheme；

Step 2: energy management：

To the energy storage device after type selecting, energy requirement forecast model is set up, according to the aeronautical data and intelligence of ship history The real time information that traffic system is provided, predicts the power demand of ship subsequent period；Set up the rolling optimization on limited period of time Strategy, to next sampling instant, according to the actual power demand of ship, the prediction to the model of ship is modified, Ran Houzai Carry out new Optimization Prediction；

Fuzzy controller is set up, the state-of-charge of energy storage device, power demand, prediction power demand are subjected to Fuzzy processing, Input fuzzy variable is formed, each input fuzzy variable is then sent to fuzzy controller；Set up energy management system for ship The control rule base of population fuzzy controller, theoretical using intelligent group, i.e., particle group optimizing method enters to fuzzy controller Row optimization.

2. the optimum management method of the energy storage device of double electric type ships according to claim 1, it is characterised in that：Described Power and energy requirement be drawn according to the statistics that works long hours at present with the work ship of target ship same type one group and The data of time correlation.

3. the optimum management method of the energy storage device of double electric type ships according to claim 1, it is characterised in that：Described Ferric phosphate lithium cell, the specifications parameter of ultracapacitor include battery capacity, battery cell weight, battery cell price and charge and discharge Electric curve；The specifications parameter of described propulsion electric machine includes total erection weight, efficiency characteristic.

4. the optimum management method of the energy storage device of double electric type ships according to claim 1, it is characterised in that：Described The calculation procedure of genetic algorithm includes：

1) two variable Xs 1, X2 are defined, real coding is carried out to the two variables；

2) population scale is set, initial population is produced according to constraints；

3) calculating of quick non-dominated ranking and virtual crowding distance is carried out to contemporary population；Wherein, quick non-dominated ranking It is to be carried out according to the ship Energy design index of each type selecting and energy storage device total price the two target function values, and it is virtual Crowding distance is drawn according to range information of the individual vector in the variable space；

4) it is the optimization aim calculated, its mathematical table to determine Energy design index E EDI, the energy storage device total price Price of ship Up to as follows：

<mrow> <mi>E</mi> <mi>E</mi> <mi>D</mi> <mi>I</mi> <mo>=</mo> <mfrac> <mrow> <mi>S</mi> <mi>P</mi> <mi>f</mi> </mrow> <mrow> <msub> <mi>f</mi> <mi>i</mi> </msub> <msub> <mi>CapacityV</mi> <mrow> <mi>r</mi> <mi>e</mi> <mi>f</mi> </mrow> </msub> <msub> <mi>f</mi> <mi>w</mi> </msub> </mrow> </mfrac> <mo>,</mo> </mrow>

Price=M_b*n₁+M_s*n₂

In formula：S is the conversion factor of carbon dioxide, and P is the power of power system, and f is correction factor, f_iTo consider ship because of skill The dimensionless correction factor that art or regulation are required and limited design maximum Loading conditions, Capacity is ship gross ton Number；V_refFor under design maximum Loading conditions, in the case that defined shaft power is promoted, in calmness sea with no wind, no waves Ship speed under condition；f_wTo consider the dimensionless factor of the influence of wave height, wave frequency and wind speed to ship speed；M_bFor battery list The price of body, M_sFor the price of electric capacity monomer, n₁For the number of battery cell, n₂For the number of electric capacity monomer；

5) genetic manipulation, including selection, intersection and variation are carried out；Select probability, recombination fraction and aberration rate are set, sub- kind is obtained Group；

6) elite retention strategy is carried out, i.e., is merged parent population with sub- population, and carries out being based on quick non-dominated ranking With the selection of virtual crowding distance, then parameter parent population of future generation；Iterations adds 1, is back to 3), until iteration time Untill number reaches the maximum of setting.

5. the optimum management method of the energy storage device of double electric type ships according to claim 1, it is characterised in that：Described In battery energy management model, mode of operation is divided as follows：

(1) when power demand is more than upper threshold values, and target vessel operation is in starting, anxious acceleration or high capacity, ultracapacitor Group and ferric phosphate lithium cell group cooperation are electric machine with energy；

(2) when power demand is between threshold values up and down, when target vessel operation is in acceleration mode, ultracapacitor group is preferentially big Electric current repid discharge provides acceleration energy for propulsion electric machine；

(3) when power demand is less than lower threshold values, when target ship works in steady steaming state, the preferential work of ferric phosphate lithium cell group Energy is provided as propulsion electric machine.

6. the optimum management method of the energy storage device of double electric type ships according to claim 1, it is characterised in that：Described Battery energy management model controls the electric discharge electricity of energy storage device according to the state-of-charge of the power demand combination battery of target ship Stream.