CN110163422A - A kind of optimization method of fuel cell generation steady-state output power and efficiency - Google Patents
A kind of optimization method of fuel cell generation steady-state output power and efficiency Download PDFInfo
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Abstract
The invention discloses the optimization methods of a kind of fuel cell generation steady-state output power and efficiency, which is characterized in that fuel cell power system model determines the decision variable for influencing electricity generation system output power, efficiency first；Then according to the constraint condition of decision variable and value range, a random population is initialized；Initial population is constantly selected using the differential evolution algorithm SMODE- ε CD of belt restraining, is intersected, mutation operation, optimizing decision variable is obtained；The optimal solution of fuel cell generation steady-state output power and efficiency is calculated according to double optimization aim formula.Invention considers the inseparability of both output power and efficiency in fuel cell system, combines the Bi-objective of output power and efficiency to optimize, and improves the power and efficiency of the output of fuel cell system stable state.
Description
Technical field
The invention belongs to fuel cell technology for power generation fields, and in particular to a kind of fuel cell generation stable state output work
The optimization method of rate and efficiency.
Background technique
Energy problem has become the important restriction factor of China's socio-economic development, concerning economic security and national security,
The Development of Novel energy has been very urgent.Fuel cell has low noise, pollution-free, zero-emission and energy conversion efficiency height etc. excellent
Point is particularly suitable for doing the power source of electric car.National governments, enterprise and scientific research institution are all dedicated to emphatically studying fuel electricity
Pond electric car, and heart of the Fuel Cell Vehicle Powertrain as fuel cell car, are currently in new breakthrough early period,
Becoming the new research and development focus in the whole world.From the point of view of existing research achievement, both at home and abroad to fuel cell, to fuel cell car
A large amount of elaborations have been carried out in research, achieve many research achievements with directive significance, and be successfully made
It is commercialized demonstrating running.But fuel cell car in China, even still belong to early stage of development in the world.Fuel cell
System lifetim, reliability, environmental suitability are there are also very big room for promotion, also just lasting increasing section in this respect, the Chinese government
Grind the investment of funds.
The load of fuel cell-driven changes when needing to improve output power, if the supply of raising fuel simply
Although output power can be improved, the loss i.e. increase of parasitic power is also resulted in, simultaneously so as to reduce fuel cell
Delivery efficiency and generate unnecessary fuel consumption.Therefore for fuel cell generation, being continuously improved, its is defeated
Out while power, optimize its delivery efficiency, to avoid the waste of resource, this gos deep into for fuel cells applications
Development has practical significance.But currently, performance study to fuel cell or concentrating on above its output power, pursue output
The maximization of power；Consumed parasitic power is reduced for the oxygen scarcity control of its air supply system proposition.
Summary of the invention
The object of the present invention is to provide the optimization methods of fuel cell generation steady-state output power and efficiency, are loading
Fuel cell system steady-state output power and efficiency can be improved when changing in condition.
The technical scheme adopted by the invention is that a kind of optimization of fuel cell generation steady-state output power and efficiency
Method, first fuel cell power system model determine the decision variable for influencing electricity generation system output power, efficiency；Then
According to the constraint condition and value range of decision variable, a random population is initialized；Using the differential evolution algorithm of belt restraining
SMODE- ε CD constantly selects initial population, intersected, mutation operation, obtains optimizing decision variable；According to double optimization aims
Formula calculates the optimal solution of fuel cell generation steady-state output power and efficiency.
The features of the present invention also characterized in that:
It is specifically implemented according to the following steps:
Step 1: fuel cell power system model, which determines, influences electricity generation system output power, the decision of efficiency becomes
Amount；Then total decision vector X is obtained；
Step 2: determining constraint condition, the value range of decision variable of total decision vector X that step 1 obtains；
Step 3: a random population, i.e. parent population are initialized according to step 2；Individual is total decision vector X；
Step 4: the initial population of step 3 is constantly selected using the differential evolution algorithm SMODE- ε CD of belt restraining,
Intersect, variation；Obtain optimal total decision vector X, i.e. optimizing decision variable；
Step 5: calculating fuel cell generation steady-state output power and efficiency most according to double optimization aim formula
Excellent solution.
In step 1, decision variable includes six parameters, respectively cathode air pressure Pca, anode hydrogen gas pressure Pan, yin
Pole mass flow Vsm, anode hydrogen gas mass flow VAn, in, compressor rotary speed ωcp, load current Ist；X is total decision vector, X=
(x1,x2,x3,x4,x5,x6), x1=Pca, x2=Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist。
Step 2 detailed process are as follows:
Determine the constraint condition of total decision vector X
It sets fuel cell generation peroxide ratio OER and is no more than λ；Fuel cell generation output power be P, then its
Peak power PVolume≤1.1P；Fuel cell generation delivery efficiency can not reach 100%；Then there is following constraint condition:
In formula,For system peroxide ratio OER；X is total decision vector, X=(x1,x2,x3,x4,x5,x6), x1=Pca, x2
=Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist；
Determine the value range of decision variable
In order to meet stable operation condition and safety requirements, cathode air pressure x1=Pca, anode hydrogen gas pressure x2=
Pan, cathode quality flow x3=Vsm, anode hydrogen gas mass flow x4=Van,in, load current x6=IstMaximum value be fuel electricity
Pond electricity generation system rated power PVolumeWith calculated result when load resistance R；The revolving speed x of compressor5=ωcpNo more than ω；Then have
Following constraint condition:
The detailed process of step 4 are as follows:
Step 4.1: parent population is determined according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, mutation algorithm；
Step 4.2: parent population is selected according to selection algorithm, crossover algorithm, mutation algorithm, is intersected, is made a variation；It generates
Progeny population；
Step 4.3: parent population, progeny population are merged；Further according to Pareto feasible solution mechanism to merging after
The individual of population is ranked up；New parent population is formed by the excellent individual to bad selection certain amount；
Step 4.4: new parent population updates according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, the mutation algorithm of new parent population；
Step 4.5: new parent population is selected according to updated selection algorithm, crossover algorithm, mutation algorithm, is handed over
Fork, variation；Generate new progeny population；
Step 4.6. judges whether population algebra is greater than the maximum algebra of setting, if so, output result；If it is not, circulation step
Rapid 4.3- step 4.5.
Detailed process is as follows for step 4.1:
(1) selection algorithm of initial random population is determined
According to control constraints formula, ε (t) value of initial random population is calculated；Wherein, control constraints formula is as follows:
ε (t)=ε (0) (1-t/Tc)cp0 < t < Tc (3)
In formula, TcFor the maximum evolutionary generation for constraining relaxation；Cp value range is [2,10]；ε (0) is initial ε value,
It is calculated by θ individual preceding in initial population, i.e. ε (0)=φ (xθ)；
(2) crossover algorithm of initial random population is determined
According to crossover operator adaptive updates formula, CR (t) value of initial random population is calculated；Wherein, crossover operator from
It is as follows to adapt to the formula updated:
CR (t)=CRmin+(CRmax-CRmin)exp(-t/gen) (4)
In formula, CRmaxFor the upper limit of CR；CRminFor the lower limit of CR；The variation range of CR is [0,1]；T is when evolution generation
Number, gen are total evolutionary generation；
(3) mutation algorithm of initial random population is determined
According to mutation operator adaptive updates formula, F (t) value of initial random population is calculated；Wherein, mutation operator is adaptive
Answer more new formula are as follows:
F (t)=Fmin+(Fmax-Fmin)exp(-t/gen) (5)
In formula, FmaxFor the F upper limit；FminFor F lower limit；The value range of F is [0,1]；T is current evolutionary generation；Gen is total
Evolutionary generation.
In step 4.3, the individual in population is ranked up using the searching method of level sequence, is followed successively by high-quality feasible
Solution, feasible solution inferior, the low infeasible solution of promise breaking degree.
In selection course, if optimal conditions are more, the unified magnitude constrained and violate is needed；P inequality constraints gj
(x) and q equality constraint hj(x), then there is following constraint condition:
In step 5, double optimization aims of fuel cell generation are as follows:
F (X)=[max (Pst),max(η)]T (7)
In formula, PstFor fuel cell steady-state output power；η is fuel cell efficiency；Pst, η be function about X, by
Fuel cell generation model obtains；T indicates transposition.
The beneficial effects of the present invention are:
(1) present invention considers the inseparability of both output power and efficiency in fuel cell system, combines defeated
Power and the Bi-objective of efficiency optimize out, improve the power and efficiency of the output of fuel cell system stable state；
(2) in the mutation process of differential evolution algorithm, using a kind of mutation operator adaptive updates of index variation
Strategy had not only kept population diversity early period but also had kept the convergence distributivity of later period optimum individual；
(3) each population rule are equally adapted to using the value of adaptive updates in the crossover process of differential evolution algorithm
Mould and characteristic more effectively more acurrate can find optimum solution.
Detailed description of the invention
Fig. 1 is the frame diagram of the optimization method of fuel cell generation steady-state output power of the present invention and efficiency；
Fig. 2 is the stream of optimization method in fuel cell generation steady-state output power of the present invention and the optimization method of efficiency
Cheng Tu；
Fig. 3 is the effect of embodiment 1 in fuel cell generation steady-state output power of the present invention and the optimization method of efficiency
Fruit comparison chart.
Specific embodiment
The following describes the present invention in detail with reference to the accompanying drawings and specific embodiments.
As shown in Figure 1, a kind of optimization method of fuel cell generation steady-state output power and efficiency of the present invention, first
Fuel cell power system model determines the decision variable for influencing electricity generation system output power, efficiency；Then according to decision
The constraint condition and value range of variable initialize a random population；Using the differential evolution algorithm SMODE- ε CD of belt restraining
Initial population is constantly selected, is intersected, mutation operation, optimizing decision variable is obtained；It is calculated according to double optimization aim formula
The optimal solution of fuel cell generation steady-state output power and efficiency out.
It is specifically implemented according to the following steps:
Step 1: fuel cell power system model, which determines, influences electricity generation system output power, the decision of efficiency becomes
Amount；Then total decision vector X is obtained；
Decision variable includes six parameters, respectively cathode air pressure Pca, anode hydrogen gas pressure Pan, cathode quality stream
Measure Vsm, anode hydrogen gas mass flow VAn, in, compressor rotary speed ωcp, load current Ist；X is total decision vector, X=(x1,x2,
x3,x4,x5,x6), x1=Pca, x2=Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist。
Step 2: determining constraint condition, the value range of decision variable of total decision vector X that step 1 obtains；
Determine the constraint condition of total decision vector X
It sets fuel cell generation peroxide ratio OER and is no more than λ；Fuel cell generation output power be P, then its
Peak power PVolume≤1.1P；Fuel cell generation delivery efficiency can not reach 100%；Then there is following constraint condition:
In formula,For system peroxide ratio OER；X is total decision vector, X=(x1,x2,x3,x4,x5,x6), x1=Pca, x2=
Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist；
Determine the value range of decision variable
In order to meet stable operation condition and safety requirements, cathode air pressure x1=Pca, anode hydrogen gas pressure x2=
Pan, cathode quality flow x3=Vsm, anode hydrogen gas mass flow x4=Van,in, load current x6=IstMaximum value be fuel electricity
Pond electricity generation system rated power PVolumeWith calculated result when load resistance R；The revolving speed x of compressor5=ωcpNo more than ω；Then have
Following constraint condition:
Step 3: a random population, i.e. parent population are initialized according to step 2；Individual is total decision vector X；
Step 4: the initial population of step 3 is constantly selected using the differential evolution algorithm SMODE- ε CD of belt restraining,
Intersect, variation；Obtain optimal total decision vector X, i.e. optimizing decision variable；
As shown in Fig. 2, detailed process are as follows:
Step 4.1: parent population is determined according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, mutation algorithm；
(1) selection algorithm of initial random population is determined
According to control constraints formula, ε (t) value of initial random population is calculated；Wherein, control constraints formula is as follows:
ε (t)=ε (0) (1-t/Tc)cp0 < t < Tc (3)
In formula, TcFor the maximum evolutionary generation for constraining relaxation；Cp value range is [2,10]；ε (0) is initial ε value,
It is calculated by θ individual preceding in initial population, i.e. ε (0)=φ (xθ)；
In selection course, if optimal conditions are more, the unified magnitude constrained and violate is needed；P inequality constraints gj
(x) and q equality constraint hj(x), then there is following constraint condition:
(2) crossover algorithm of initial random population is determined
According to crossover operator adaptive updates formula, CR (t) value of initial random population is calculated；Wherein, crossover operator from
It is as follows to adapt to the formula updated:
CR (t)=CRmin+(CRmax-CRmin)exp(-t/gen) (4)
In formula, CRmaxFor the upper limit of CR；CRminFor the lower limit of CR；The variation range of CR is [0,1]；T is when evolution generation
Number, gen are total evolutionary generation；
(3) mutation algorithm of initial random population is determined
According to mutation operator adaptive updates formula, F (t) value of initial random population is calculated；Wherein, mutation operator is adaptive
Answer more new formula are as follows:
F (t)=Fmin+(Fmax-Fmin)exp(-t/gen) (5)
In formula, FmaxFor the F upper limit；FminFor F lower limit；The value range of F is [0,1]；T is current evolutionary generation；Gen is total
Evolutionary generation.
Step 4.2: parent population is selected according to selection algorithm, crossover algorithm, mutation algorithm, is intersected, is made a variation；It generates
Progeny population；
Step 4.3: parent population, progeny population are merged；Further according to Pareto feasible solution mechanism to merging after
The individual of population is ranked up；New parent population is formed by the excellent individual to bad selection certain amount；Specifically, using horizontal row
The searching method of sequence is ranked up the individual in population, be followed successively by high-quality feasible solution, feasible solution inferior, promise breaking degree is low can not
Row solution.
Step 4.4: new parent population updates according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, the mutation algorithm of new parent population；
Step 4.5: new parent population is selected according to updated selection algorithm, crossover algorithm, mutation algorithm, is handed over
Fork, variation；Generate new progeny population；
Step 4.6. judges whether population algebra is greater than the maximum algebra of setting, if so, output result；If it is not, circulation step
Rapid 4.3- step 4.5.
Step 5: calculating fuel cell generation steady-state output power and efficiency most according to double optimization aim formula
Excellent solution；
Double optimization aims of fuel cell generation are as follows:
F (X)=[max (Pst),max(η)]T (7)
In formula, PstFor fuel cell steady-state output power；η is fuel cell efficiency；Pst, η be function about X, by
Fuel cell generation model obtains；T indicates transposition.
Embodiment 1
Step 1: fuel cell power system model, which determines, influences electricity generation system output power, the decision of efficiency becomes
Amount；Then total decision vector X is obtained；
Decision variable includes six parameters, respectively cathode air pressure Pca, anode hydrogen gas pressure Pan, cathode quality stream
Measure Vsm, anode hydrogen gas mass flow VAn, in, compressor rotary speed ωcp, load current Ist；X is total decision vector, X=(x1,x2,
x3,x4,x5,x6), x1=Pca, x2=Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist。
Step 2: determining constraint condition, the value range of decision variable of total decision vector X that step 1 obtains；
Determine the constraint condition of total decision vector X
It sets fuel cell generation peroxide ratio OER and is no more than 2.4；Fuel cell generation output power is
50kW, then its peak power is no more than 55kW；Fuel cell generation delivery efficiency can not reach 100%；Then have as follows
Constraint condition:
In formula,For system peroxide ratio OER；X is total decision vector, X=(x1,x2,x3,x4,x5,x6), x1=Pca, x2=
Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist；
Determine the value range of decision variable
In order to meet stable operation condition and safety requirements, cathode air pressure x1=Pca, anode hydrogen gas pressure x2=
Pan, cathode quality flow x3=Vsm, anode hydrogen gas mass flow x4=Van,in, load current x6=IstMaximum value be fuel electricity
Calculated result when the electricity generation system rated power 50kW of pond；The revolving speed x of compressor5=ωcpNo more than 2000r/min；Then just like
Lower constraint condition:
Step 3: a random population, i.e. parent population are initialized according to step 2；Individual is total decision vector X；Setting kind
Group's size is 250,
Step 4: the initial population of step 3 is constantly selected using the differential evolution algorithm SMODE- ε CD of belt restraining,
Intersect, variation；Obtain optimal total decision vector X, i.e. optimizing decision variable；Maximum number of iterations gen is set as 100；
Detailed process is as follows:
Step 4.1: parent population is determined according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, mutation algorithm；
(1) selection algorithm of initial random population is determined
According to control constraints formula, ε (t) value of initial random population is calculated；Wherein, control constraints formula is as follows:
ε (t)=ε (0) (1-t/Tc)cp0 < t < Tc (3)
In formula, TcFor the maximum evolutionary generation for constraining relaxation；Cp value range is [2,10]；ε (0) is initial ε value,
It is calculated by θ individual preceding in initial population, i.e. ε (0)=φ (xθ)；
In selection course, if optimal conditions are more, the unified magnitude constrained and violate is needed；P inequality constraints gj
(x) and q equality constraint hj(x), then there is following constraint condition:
(2) crossover algorithm of initial random population is determined
According to crossover operator adaptive updates formula, CR (t) value of initial random population is calculated；Wherein, crossover operator from
It is as follows to adapt to the formula updated:
CR (t)=CRmin+(CRmax-CRmin)exp(-t/gen) (4)
In formula, CRmaxFor the upper limit of CR；CRminFor the lower limit of CR；The variation range of CR is [0,1]；T is when evolution generation
Number, gen are total evolutionary generation；
(3) mutation algorithm of initial random population is determined
According to mutation operator adaptive updates formula, F (t) value of initial random population is calculated；Wherein, mutation operator is adaptive
Answer more new formula are as follows:
F (t)=Fmin+(Fmax-Fmin)exp(-t/gen) (5)
In formula, FmaxFor the F upper limit；FminFor F lower limit；The value range of F is [0,1]；T is current evolutionary generation；Gen is total
Evolutionary generation；
Step 4.2: parent population is selected according to selection algorithm, crossover algorithm, mutation algorithm, is intersected, is made a variation；It generates
Progeny population；
Step 4.3: parent population, progeny population are merged；Further according to Pareto feasible solution mechanism to merging after
The individual of population is ranked up；New parent population is formed by the excellent individual to bad selection certain amount；Specifically, using horizontal row
The searching method of sequence is ranked up the individual in population, be followed successively by high-quality feasible solution, feasible solution inferior, promise breaking degree is low can not
Row solution.
Step 4.4: new parent population updates according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Selection algorithm, crossover algorithm, the mutation algorithm of new parent population；
Step 4.5: new parent population is selected according to updated selection algorithm, crossover algorithm, mutation algorithm, is handed over
Fork, variation；Generate new progeny population；
Step 4.6. judges whether population algebra is greater than the maximum algebra of setting, if so, output result；If it is not, circulation step
Rapid 4.3- step 4.5.
Step 5: calculating fuel cell generation steady-state output power and efficiency most according to double optimization aim formula
Excellent solution；
Double optimization aims of fuel cell generation are as follows:
F (X)=[max (Pst),max(η)]T (1)
In formula, PstFor fuel cell steady-state output power；η is fuel cell efficiency；Pst, η be function about X, by
Fuel cell generation model obtains；T indicates transposition；
For the peak power output of fuel cell generation up to 65.2KW, corresponding to maximal efficiency at this time is 85.2%.Such as
It is the effect comparison chart of embodiment 1 shown in Fig. 3.
Embodiment 2
Step 1: identical as the step 1 of embodiment 1；
Step 2: roughly the same with the step 2 of embodiment 1, difference is only that: fuel cell generation output power is
40kW, then its peak power is no more than 44kW；
Step 3: roughly the same with the step 3 of embodiment 1, difference is only that: setting Population Size as 200；
Step 4: roughly the same with the step 4 of embodiment 1, difference is only that: setting maximum number of iterations gen as 120；
Step 5: identical as the step 5 of embodiment 1, difference is only that: the peak power output of fuel cell generation
Nearly 50KW, corresponding to maximal efficiency at this time is 85.6%.
Embodiment 3
Step 1: identical as the step 1 of embodiment 1；
Step 2: roughly the same with the step 2 of embodiment 1, difference is only that: fuel cell generation output power is
60kW, then its peak power is no more than 66kW；
Step 3: roughly the same with the step 3 of embodiment 1, difference is only that: setting Population Size as 300；
Step 4: roughly the same with the step 4 of embodiment 1, difference is only that: setting maximum number of iterations gen as 120；
Step 5: identical as the step 5 of embodiment 1, difference is only that: the peak power output of fuel cell generation
Nearly 74KW, corresponding to maximal efficiency at this time is 85.7%.
Claims (9)
1. a kind of optimization method of fuel cell generation steady-state output power and efficiency, which is characterized in that first according to combustion
Expect that battery generating system model determines the decision variable for influencing electricity generation system output power, efficiency；Then according to decision variable
Constraint condition and value range initialize a random population；Using the differential evolution algorithm SMODE- ε CD of belt restraining to initial
Population constantly selected, intersected, mutation operation, obtains optimizing decision variable；Fuel is calculated according to double optimization aim formula
The optimal solution of battery generating system steady-state output power and efficiency.
2. the optimization method of fuel cell generation steady-state output power and efficiency as described in claim 1, feature exist
In being specifically implemented according to the following steps:
Step 1: fuel cell power system model determines the decision variable for influencing electricity generation system output power, efficiency；After
And obtain total decision vector X；
Step 2: determining constraint condition, the value range of decision variable of total decision vector X that step 1 obtains；
Step 3: a random population, i.e. parent population are initialized according to step 2；Individual is total decision vector X；
Step 4: the initial population of step 3 constantly being selected using the differential evolution algorithm SMODE- ε CD of belt restraining, is handed over
Fork, variation；Obtain optimal total decision vector X, i.e. optimizing decision variable；
Step 5: the optimal solution of fuel cell generation steady-state output power and efficiency is calculated according to double optimization aim formula.
3. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 1 or 2, feature
It is, in the step 1, decision variable includes six parameters, respectively cathode air pressure Pca, anode hydrogen gas pressure Pan, yin
Pole mass flow Vsm, anode hydrogen gas mass flow VAn, in, compressor rotary speed ωcp, load current Ist；X is total decision vector, X=
(x1,x2,x3,x4,x5,x6), x1=Pca, x2=Pan, x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist。
4. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 3, feature exist
In step 2 detailed process are as follows:
Determine the constraint condition of total decision vector X
It sets fuel cell generation peroxide ratio OER and is no more than λ；Fuel cell generation output power is P, then its peak value
Power PVolume≤1.1P；Fuel cell generation delivery efficiency can not reach 100%；Then there is following constraint condition:
In formula, λO2For system peroxide ratio OER；X is total decision vector, X=(x1,x2,x3,x4,x5,x6), x1=Pca, x2=Pan,
x3=Vsm, x4=Van,in, x5=ωcp, x6=Ist；
Determine the value range of decision variable
In order to meet stable operation condition and safety requirements, cathode air pressure x1=Pca, anode hydrogen gas pressure x2=Pan, yin
Pole mass flow x3=Vsm, anode hydrogen gas mass flow x4=Van,in, load current x6=IstMaximum value be fuel cell hair
Electric system rated power PVolumeWith calculated result when load resistance R；The revolving speed x of compressor5=ωcpNo more than ω；Then have as follows
Constraint condition:
5. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 4, feature exist
In the detailed process of the step 4 are as follows:
Step 4.1: the selection of parent population is determined according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
Algorithm, crossover algorithm, mutation algorithm；
Step 4.2: parent population is selected according to selection algorithm, crossover algorithm, mutation algorithm, is intersected, is made a variation；Generate filial generation
Population；
Step 4.3: parent population, progeny population are merged；Further according to Pareto feasible solution mechanism to the population after merging
Individual be ranked up；New parent population is formed by the excellent individual to bad selection certain amount；
Step 4.4: new parent population updates new father according to control constraints formula, the adaptive formula of intersection, the adaptive formula of variation
For the selection algorithm, crossover algorithm, mutation algorithm of population；
Step 4.5: new parent population is selected according to updated selection algorithm, crossover algorithm, mutation algorithm, is intersected, is become
It is different；Generate new progeny population；
Step 4.6. judges whether population algebra is greater than the maximum algebra of setting, if so, output result；If it is not, circulation step
4.3- step 4.5.
6. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 5, feature exist
In detailed process is as follows for the step 4.1:
(1) selection algorithm of initial random population is determined
According to control constraints formula, ε (t) value of initial random population is calculated；Wherein, control constraints formula is as follows:
ε (t)=ε (0) (1-t/Tc)cp0 < t < Tc (3)
In formula, TcFor the maximum evolutionary generation for constraining relaxation；Cp value range is [2,10]；ε (0) is initial ε value, by first
Preceding θ individual is calculated in beginning population, i.e. ε (0)=φ (xθ)；
(2) crossover algorithm of initial random population is determined
According to crossover operator adaptive updates formula, CR (t) value of initial random population is calculated；Wherein, crossover operator is adaptive
The formula of update is as follows:
CR (t)=CRmin+(CRmax-CRmin)exp(-t/gen) (4)
In formula, CRmaxFor the upper limit of CR；CRminFor the lower limit of CR；The variation range of CR is [0,1]；T is current evolutionary generation,
Gen is total evolutionary generation；
(3) mutation algorithm of initial random population is determined
According to mutation operator adaptive updates formula, F (t) value of initial random population is calculated；Wherein, mutation operator is adaptively more
New formula are as follows:
F (t)=Fmin+(Fmax-Fmin)exp(-t/gen) (5)
In formula, FmaxFor the F upper limit；FminFor F lower limit；The value range of F is [0,1]；T is current evolutionary generation；Gen be it is total into
Change algebra.
7. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 6, feature exist
In, in the step 4.3, using level sequence searching method the individual in population is ranked up, be followed successively by high-quality feasible
Solution, feasible solution inferior, the low infeasible solution of promise breaking degree.
8. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 7, feature exist
In if optimal conditions are more, needing the unified magnitude constrained and violate in the selection course；P inequality constraints gj
(x) and q equality constraint hj(x), then there is following constraint condition:
9. the optimization method of fuel cell generation steady-state output power and efficiency as claimed in claim 8, feature exist
In, in the step 5, double optimization aims of fuel cell generation are as follows:
F (X)=[max (Pst),max(η)]T (7)
In formula, PstFor fuel cell steady-state output power；η is fuel cell efficiency；Pst, η be function about X, by fuel
Battery generating system model obtains；T indicates transposition.
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