CN109376437A - A kind of vehicle-mounted energy storage optimal control method of new energy based on multi-objective genetic algorithm - Google Patents
A kind of vehicle-mounted energy storage optimal control method of new energy based on multi-objective genetic algorithm Download PDFInfo
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Abstract
The present invention is based on the vehicle-mounted energy storage optimal control method of the new energy of multi-objective genetic algorithm, step includes: to establish the control strategy library of vehicle-mounted energy-storage system；Establish the characteristic module of vehicles energy accumulating system；The evaluation module for establishing energy-storage system under various factors coupling evaluates superiority-inferiority of the current control strategy under life cycle management；Energy saving optimizing is carried out to each control strategy using multi-objective genetic algorithm, the optimal control policy of the vehicle-mounted energy storage of new energy under various factors coupling is obtained by the effect of each control strategy of comparative analysis.For the present invention under the conditions of comfort of passenger is protected, the speed command of design can realize traction energy conservation；The speed command that final design obtains reaches the requirement of non-dominant standard, highly beneficial to the decision for being uniformly distributed standard and energy consumption sensitiveness standard based on the time；Energy saving optimizing, by its effect of comparative analysis, the optimal control policy of available vehicle-mounted energy-storage system are carried out for the speed command under a variety of energy-storage system control strategies.
Description
Technical field
The present invention relates to urban rail transit technology field, especially a kind of vehicle-mounted energy storage of urban track traffic new energy is excellent
Change control method.
Background technique
As Green Travel theory is increasingly rooted in the hearts of the people, new energy tramcar has obtained quick popularization, but new energy
Source vehicle energy-storage system and operation control do not comprehensively consider energy-storage system service life, energy utilization efficiency and vehicle operation energy
Consumption.Currently, the research hotspot of new energy tramcar is to obtain taking into account the vehicle-mounted storage of multiple target with computer aided decision making
It can system optimized control method.
At present to the research of vehicle-mounted energy-storage system optimal control method, the utilization rate for promoting energy-storage system is focused primarily upon,
I.e. in the case where meeting route normal operation, the utilization rate of energy-storage system is promoted as much as possible.It is mainly reflected in simulation modeling
During using the utilization rate of energy-storage system as evaluation index, write computer aided decision making algorithm and go Optimal Control Strategy.
The energy-saving run research of existing new energy vehicle has the following problems: (1) the optimization control of existing vehicle-mounted energy-storage system
Method processed focuses primarily upon the capacity usage ratio for improving energy-storage system, does not take into account energy-storage system service life, energy utilization
Efficiency and vehicle operation energy consumption；(2) influence of the vehicle-mounted energy-storage system control strategy to energy-saving driving curve is not accounted for；(3)
Track, train and driving model are simplified in the course of the research not over numerical method, it can not be to each vehicle-mounted energy storage system
The operation energy consumption of system control strategy Train is accurately calculated.
Summary of the invention
The purpose of the present invention provides a kind of based on multi-objective genetic algorithm primarily directed to above-mentioned problem of the prior art
The vehicle-mounted energy storage optimal control method of new energy.
In order to solve the above technical problems, the vehicle-mounted energy storage of the new energy provided by the invention based on multi-objective genetic algorithm is excellent
Change control method comprising the steps of:
Step 1, the control strategy library of vehicle-mounted energy-storage system is established；
Step 2, the characteristic module of vehicles energy accumulating system is established；
Step 3, the evaluation module for establishing energy-storage system under various factors coupling evaluates current control strategy in life cycle management
Under superiority-inferiority；
Step 4, energy saving optimizing is carried out to each control strategy using multi-objective genetic algorithm, plan is respectively controlled by comparative analysis
Effect slightly, obtains the optimal control policy of the vehicle-mounted energy storage of new energy under various factors coupling.
Compared with prior art, the present invention its remarkable advantage is:
1, under the conditions of comfort of passenger is protected, energy consumption in train journey and energy storage are considered in desin speed order
System control strategy, that is, the speed command designed has energy-saving effect, it can be achieved that traction energy conservation；
2, genetic algorithm is designed applied to speed command, using quick non-dominated ranking algorithm, crowding distance and crowded
Comparison operator and elite and fitness sharing policy are spent, so that the speed command that final design obtains reaches non-dominant standard
It is required that while solve the disaggregation that is evenly distributed, to the decision for being uniformly distributed standard and energy consumption sensitiveness standard based on the time
It is highly beneficial；
3, energy saving optimizing is carried out for the speed command under a variety of energy-storage system control strategies, passes through its effect of comparative analysis
Fruit, the optimal control policy of available vehicle-mounted energy-storage system.
Detailed description of the invention
Fig. 1 is that the present invention is based on the vehicle-mounted energy storage optimal control method module diagrams of the new energy of multi-objective genetic algorithm.
Fig. 2 is vehicle-mounted energy-storage system operation emulation module schematic diagram in the present invention.
Fig. 3 is the flow diagram that NSGA-II solving speed order Pareto is solved in the present invention.
Specific embodiment
Embodiments of the present invention are illustrated with reference to the accompanying drawing.
In conjunction with Fig. 1, the vehicle-mounted energy storage optimal control method of new energy of the embodiment of the present invention based on multi-objective genetic algorithm, packet
Containing following steps:
Step 1, the control strategy library of vehicle-mounted energy-storage system is established.
In this step, the control strategy of energy-storage system is generated in bulk and is stored in control strategy library, wherein energy storage system
Control strategy of uniting includes the setting of current limit curve and the setting of voltage operation value, in which: voltage operation value is two-way when including traction
The upper limit value U of DC-DC converter operation voltagedcPmax, traction when bidirectional DC-DC converter operation voltage lower limit value UdcPmin、
The upper limit value U of bidirectional DC-DC converter operation voltage when brakingdcBmax, braking when bidirectional DC-DC converter operation voltage under
Limit value UdcBmin。
Step 2, the characteristic module of vehicles energy accumulating system is established.
The characteristic module of vehicles energy accumulating system includes track data module, train operating data module, train attribute
Data module and vehicle-mounted energy-storage system data module, this four modules are all data input modules, are optimized for vehicle-mounted energy-storage system
Control provides basic data, in which:
Track data module provides gate position data, electric substation's position data, ramp position data and the bend of route
Position data；
Train operating data module, table data and Train-borne recorder data at the time of all trains of working line are provided；
Train attribute data module, provides the basic operating conditions of train, including train traction braking characteristic, passenger capacity,
Marshals data, trailer data and drag parameter；
Vehicle-mounted energy-storage system data module provides capacity configuration, charge-discharge characteristic and the life consumption of energy-storage system.
Step 3, the evaluation module for establishing energy-storage system under various factors coupling evaluates current control strategy in life cycle management
Under superiority-inferiority.
In conjunction with Fig. 2, in this step, vehicle-mounted energy-storage system operation emulation module is established, comprising:
Vehicular speeds bidding model: being calculated the acceleration of train by input data, realizes the transfer or holding of train operating condition,
The status data of train is passed into train model, operation computation model simultaneously；
Train model: the acceleration information provided according to vehicular speeds bidding model carries out tractive force of train or brake force
Calculating, and by value of thrust or braking force value pass to operation computation model；
Run computation model: the data provided according to vehicular speeds bidding model, train model carry out dynamics operation,
It determines train present speed, range ability, runing time and realtime power, and calculated result is passed into energy-storage system control mould
Type and energy consumption, time computation model；
Energy-storage system Controlling model: according to the realtime power of operation computing module input, according to energy-storage system control strategy
The charging moment and level of charge of vehicle-mounted energy-storage system are selected, while energy storage submodule each in energy-storage system is filled
Discharge voltage and electric current are calculated, and result is output to energy consumption, time computation model；
The data that energy consumption, time computation model are provided according to operation computation model and energy-storage system Controlling model, calculate
The runing time of train and the energy consumption of energy-storage system, while the life consumption of energy-storage system is calculated.
Step 4, energy saving optimizing is carried out to each control strategy using multi-objective genetic algorithm, plan is respectively controlled by comparative analysis
Effect slightly, obtains the optimal control policy of the vehicle-mounted energy storage of new energy under various factors coupling.As shown in figure 3, step 4 specifically walks
It is rapid as follows:
A1, random initializtion father population: setting algebra t=0, and according to the constraint condition of each variable, energy storage strategy is randomly generated
Superior vector x forms initial father population Pt, and all individuals is sorted by non-dominant relationship and specify fitness value, value etc.
In the non-dominant grade of individual；
A2, initial father's population at individual fitness value calculation: passing to fitness for father population Pt and calculate sub-process, by its meter
The fitness value of each individual of population is calculated, returns to main flow after sub-process；
A3, judge whether to restrain: whether computer algebra reaches greatest iteration algebra, if reaching, terminates entire program；
A4, genetic manipulation: generating sub- population Qt by genetic manipulation, and wherein genetic manipulation mainly includes selection, intersects and become
It is different；
Selection operation uses algorithm of tournament selection operator；
Crossover operation is intersected using simulation binary system, is defined as: for two fathers individual p1 and p2, give birth in the following manner
At two sub- individual c1,kAnd c2,k:
Wherein, ci,kIt is k-th of sub- real number of i-th of individual, pi,kIt is k-th of father's real number that i-th of individual is chosen, βkFor
Random number, it is every it is one-dimensional on require to regenerate, calculation formula is as follows:
In formula, random number u is uniformly distributed generation by (0,1)；ηcIt is cross parameter, is a constant；
Mutation operation is made a variation using multinomial, it may be assumed that
In formula, ckIt is k-th of sub- real number, pkIt is k-th of father's real number,It is k-th of coboundary,It is k-th of lower boundary,
δkIt is to be generated by multinomial distribution, it may be assumed that
rkIt is that (0,1) is uniformly distributed the number generated at random, ηmIt is variation profile exponent.
A5, sub- population's fitness function calculate: sub- population Qt being passed to fitness and calculates sub-process, calculates kind by it
The fitness value of each individual of group returns to main flow after sub-process；
A6, elite and fitness value sharing policy: elite and fitness value sharing policy function is called to obtain t+1 godfather
Population Pt+1；
A7, algebra: t=t+1, jump procedure a3 is updated.
Wherein, in step a5, specific step is as follows for sub- population's fitness calculating:
B1, i-th of individual in population is taken to calculate its corresponding energy-storage system characteristic value, i initial value is 0, is closed by mapping
The chromosome of individual is converted energy-storage system characteristic value by system；
B2, check whether energy-storage system characteristic value meets simulation requirements: i.e. under coasting or mixed mode, coasting terminates speed
Be greater than coasting starting velocity, if not satisfied, then individual fitness 1, fitness 2 and fitness 3 take respectively Mmax, Nmax and
Pmax, and jump procedure b6；
The parameter of b3, the vehicle-mounted energy-storage system operation emulation module of setting；
B4, vehicle-mounted energy-storage system operation emulation is carried out；
B5, the fitness value for saving individual: individual fitness 1 indicates energy-storage system utilization rate, individual 2 table of fitness
Show that energy-storage system is lost, ideal adaptation angle value 3 indicates vehicle operation energy consumption；
Whether b6, interpretation current individual are the last one individual m in population, if condition is set up, calculate end and return,
Otherwise, i=i+1, jump procedure b1.
In addition to the implementation, the present invention can also have other embodiments.It is all to use equivalent substitution or equivalent transformation shape
At technical solution, fall within the scope of protection required by the present invention.
Claims (6)
1. a kind of vehicle-mounted energy storage optimal control method of new energy based on multi-objective genetic algorithm comprising the steps of:
Step 1, the control strategy library of vehicle-mounted energy-storage system is established；
Step 2, the characteristic module of vehicles energy accumulating system is established；
Step 3, the evaluation module for establishing energy-storage system under various factors coupling evaluates current control strategy under life cycle management
Superiority-inferiority；
Step 4, energy saving optimizing is carried out to each control strategy using multi-objective genetic algorithm, passes through each control strategy of comparative analysis
Effect obtains the optimal control policy of the vehicle-mounted energy storage of new energy under various factors coupling.
2. the vehicle-mounted energy storage optimal control method of the new energy according to claim 1 based on multi-objective genetic algorithm, special
Sign is: in the step 1, generating the control strategy of energy-storage system in bulk and is stored in control strategy library, wherein energy storage
System control strategy includes the setting of current limit curve and the setting of voltage operation value, in which: double when voltage operation value includes traction
To the upper limit value U of DC-DC converter operation voltagedcPmax, traction when bidirectional DC-DC converter operation voltage lower limit value
UdcPmin, braking when bidirectional DC-DC converter operation voltage upper limit value UdcBmax, braking when bidirectional DC-DC converter movement electricity
The lower limit value U of pressuredcBmin。
3. the vehicle-mounted energy storage optimal control method of the new energy according to claim 1 based on multi-objective genetic algorithm, special
Sign is: the characteristic module of the vehicles energy accumulating system includes track data module, train operating data module, train category
Property data module and vehicle-mounted energy-storage system data module, this four modules are all data input modules, are that vehicle-mounted energy-storage system is excellent
Change control and basic data be provided, in which:
Track data module provides gate position data, electric substation's position data, ramp position data and the bend position of route
Data；
Train operating data module, table data and Train-borne recorder data at the time of all trains of working line are provided；
Train attribute data module provides the basic operating conditions of train, including train traction braking characteristic, passenger capacity, marshalling
Data, trailer data and drag parameter；
Vehicle-mounted energy-storage system data module provides capacity configuration, charge-discharge characteristic and the life consumption of energy-storage system.
4. the vehicle-mounted energy storage optimal control method of the new energy according to claim 1 based on multi-objective genetic algorithm, special
Sign is: in step 3, establishing vehicle-mounted energy-storage system operation emulation module, comprising:
Vehicular speeds bidding model: being calculated the acceleration of train by input data, realizes the transfer or holding of train operating condition, simultaneously
The status data of train is passed into train model, operation computation model；
Train model: the acceleration information provided according to vehicular speeds bidding model carries out the meter of tractive force of train or brake force
It calculates, and value of thrust or braking force value is passed into operation computation model；
Run computation model: the data provided according to vehicular speeds bidding model, train model carry out dynamics operation, determine
Train present speed, range ability, runing time and realtime power, and by calculated result pass to energy-storage system Controlling model and
Energy consumption, time computation model；
Energy-storage system Controlling model: according to the realtime power of operation computing module input, according to energy-storage system control strategy to vehicle
The charging moment and level of charge for carrying energy-storage system are selected, while the charge and discharge to energy storage submodule each in energy-storage system
Voltage and current is calculated, and result is output to energy consumption, time computation model；
The data that energy consumption, time computation model are provided according to operation computation model and energy-storage system Controlling model, calculate train
Runing time and energy-storage system energy consumption, while the life consumption of energy-storage system is calculated.
5. the vehicle-mounted energy storage optimal control method of the new energy according to claim 1 based on multi-objective genetic algorithm, special
Sign is: specific step is as follows for the step 4:
A1, random initializtion father population: setting algebra t=0, and according to the constraint condition of each variable, energy storage policy optimization is randomly generated
Vector x, forms initial father population Pt, and all individuals are sorted and specified fitness value by non-dominant relationship, and value is equal to
The non-dominant grade of body；
A2, initial father's population at individual fitness value calculation: father population Pt is passed into fitness and calculates sub-process, is calculated by it
The fitness value of each individual of population returns to main flow after sub-process；
A3, judge whether to restrain: whether computer algebra reaches greatest iteration algebra, if reaching, terminates entire program；
A4, genetic manipulation: generating sub- population Qt by genetic manipulation, and wherein genetic manipulation mainly includes selection, intersects and make a variation；
Selection operation uses algorithm of tournament selection operator；
Crossover operation is intersected using simulation binary system, is defined as: for two fathers individual p1 and p2, two are generated in the following manner
Height individual c1,kAnd c2,k:
Wherein, ci,kIt is k-th of sub- real number of i-th of individual, pi,kIt is k-th of father's real number that i-th of individual is chosen, βkIt is random
Number, it is every it is one-dimensional on require to regenerate, calculation formula is as follows:
In formula, random number u is uniformly distributed generation by (0,1)；ηcIt is cross parameter, is a constant；
Mutation operation is made a variation using multinomial, it may be assumed that
In formula, ckIt is k-th of sub- real number, pkIt is k-th of father's real number,It is k-th of coboundary,It is k-th of lower boundary, δkIt is
It is generated by multinomial distribution, it may be assumed that
rkIt is that (0,1) is uniformly distributed the number generated at random, ηmIt is variation profile exponent.
A5, sub- population's fitness function calculate: sub- population Qt being passed to fitness and calculates sub-process, it is every to calculate population by it
The fitness value of individual returns to main flow after sub-process；
A6, elite and fitness value sharing policy: elite and fitness value sharing policy function is called to obtain t+1 godfather population
Pt+1；
A7, algebra: t=t+1, jump procedure a3 is updated.
6. the vehicle-mounted energy storage optimal control method of the new energy according to claim 5 based on multi-objective genetic algorithm, special
Sign is: in step a5, specific step is as follows for sub- population's fitness calculating:
B1, i-th of individual in population is taken to calculate its corresponding energy-storage system characteristic value, i initial value is 0, will by mapping relations
The chromosome of individual is converted into energy-storage system characteristic value；
B2, check whether energy-storage system characteristic value meets simulation requirements: i.e. under coasting or mixed mode, it is big that coasting terminates speed
In coasting starting velocity, if not satisfied, then individual fitness 1, fitness 2 and fitness 3 take respectively Mmax, Nmax and
Pmax, and jump procedure b6；
The parameter of b3, the vehicle-mounted energy-storage system operation emulation module of setting；
B4, vehicle-mounted energy-storage system operation emulation is carried out；
B5, the fitness value for saving individual: individual fitness 1 indicates energy-storage system utilization rate, and individual fitness 2 indicates storage
Energy system loss, ideal adaptation angle value 3 indicate vehicle operation energy consumption；
Whether b6, interpretation current individual are the last one individual m in population, if condition is set up, calculate end and return, no
Then, i=i+1, jump procedure b1.
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| CN117968774B (en) * | 2024-03-28 | 2024-07-02 | 武汉市豪迈电力自动化技术有限责任公司 | Intelligent monitoring and control system for traction paying-off operation of overhead transmission line |
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