CN108437822A - A kind of fuel cell hybrid vehicle multiobjective optimization control method - Google Patents
A kind of fuel cell hybrid vehicle multiobjective optimization control method Download PDFInfo
- Publication number
- CN108437822A CN108437822A CN201810214850.XA CN201810214850A CN108437822A CN 108437822 A CN108437822 A CN 108437822A CN 201810214850 A CN201810214850 A CN 201810214850A CN 108437822 A CN108437822 A CN 108437822A
- Authority
- CN
- China
- Prior art keywords
- fuel cell
- function
- hybrid vehicle
- pbat
- pfc
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/30—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling fuel cells
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L58/00—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles
- B60L58/10—Methods or circuit arrangements for monitoring or controlling batteries or fuel cells, specially adapted for electric vehicles for monitoring or controlling batteries
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B61—RAILWAYS
- B61C—LOCOMOTIVES; MOTOR RAILCARS
- B61C3/00—Electric locomotives or railcars
- B61C3/02—Electric locomotives or railcars with electric accumulators
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/40—Application of hydrogen technology to transportation, e.g. using fuel cells
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Geometry (AREA)
- Mechanical Engineering (AREA)
- Transportation (AREA)
- General Physics & Mathematics (AREA)
- Sustainable Development (AREA)
- Life Sciences & Earth Sciences (AREA)
- Power Engineering (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Sustainable Energy (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Aviation & Aerospace Engineering (AREA)
- Automation & Control Theory (AREA)
- Fuel Cell (AREA)
Abstract
The present invention discloses a kind of fuel cell hybrid vehicle multiobjective optimization control method, realize optimization and the control field that satisfactory optimization thought is introduced to fuel cell hybrid system, complete the fuel cell hybrid system muti-criteria satisfactory optimization model of layering progressive Optimization Framework and meter and power source durability and fuel economy based on target priority degree, the final thought that Pang Te lia kings minimal principle (PMP) is utilized, optimum allocation has been carried out to bearing power on controlled [Pfc, Pbat] feasible zone.Invention core is to use the thought of satisfactory optimization, has obscured fixed constraints and restrained boundary, avoided appearance of the optimization without solution situation；It uses and is layered progressive Optimization Framework, reduce the feasible zone range of [Pfc, Pbat] step by step, finally reduce calculation amount for PMP algorithm optimizing.
Description
Technical field
The invention belongs to hybrid vehicle technology fields, more particularly to a kind of more mesh of fuel cell hybrid vehicle
Mark optimal control method.
Background technology
At present in field of track traffic, the application of the application and fuel cell hybrid locomotive of fuel cell technology
It is the hot spot of Recent study.Fuel cell has lasting generating capacity, but its dynamic response relative delay, cannot be satisfied machine
The demand etc. of vehicle accelerator momentary high power, it is auxiliary power source and energy storage device then to increase power battery, makes up fuel
The deficiency of battery dynamic response, the problems such as effectively avoiding locomotive acceleration capacity poor, recycle locomotive in braking by electricity as far as possible
Machine inverts the braking energy that feedback is returned.
The energy management method of fuel cell hybrid system be fuel cell hybrid area research hot spot and
It is crucial.Its main target is to study that the power output how coordinated between multi power source matches situation to promote fuel cell and lithium
The durability of battery makes the two have longer service life；Vehicle economy is promoted, fuel consumption is reduced, reduces O＆M
Cost.
The energy management method of existing fuel cell hybrid system can be classified as using and two kinds of segregation reasons online.
It is wherein online to consume minimum principle using including being based on state machine principle, fuzzy rule, Pang Te lia kings minimal principle, equivalent hydrogen
Deng a variety of methods；Segregation reasons include then based on Dynamic Programming Idea including a variety of optimization methods.Dynamic Programming is particular about
The minimum of object function, i.e. best performance point are got under free position, and is not required in practice, also can not
The best performance centainly obtained.
Existing fuel cell hybrid rail traffic vehicles power source durability present in longtime running is poor, clothes
Use as a servant that the service life is shorter, and fuel consumption still has optimization space, the problems such as lacking the operating condition design planning of science.
Invention content
To solve the above-mentioned problems, the present invention proposes a kind of fuel cell hybrid vehicle multiobjective optimal control side
Method, it is poor to solve fuel cell hybrid rail traffic vehicles power source durability present in longtime running, clothes
Use as a servant that the service life is shorter, and fuel consumption still has optimization space, the problems such as lacking the operating condition design planning of science.The present invention passes through
Satisfactory optimization thought is introduced, Optimization Framework is re-established, the satisfaction of each individual index is weighted, comprehensive satisfaction letter is established
Number, and as object function, can reflect the comprehensive benefit situation under different load power distribution；According to optimization side step by step
Method, available power distribute feasible zone, then carry out load power distribution using PMP methods in real-time control at any time,
PMP methods have the characteristics that real-time, easily realize, is simple；It is available more preferably using the finally obtained sharing of load of this method
Vehicle economy, power source durability, can more scientific ground planning operation whole process operating mode situation.
In order to achieve the above objectives, the technical solution adopted by the present invention is：A kind of fuel cell hybrid vehicle multiple target
Optimal control method, including step：
S100 establishes all power-efficient domains of fuel cell hybrid vehicle；
S200 passes through satisfactory optimization, further accurate and feasible domain range；
S300 is optimized by optimum control, determines the optimum allocation of load power.
Further, in the step S100, all power-efficient domains of fuel cell hybrid vehicle are established, are wrapped
Include step：
The whole actual measurement operating mode Pload-t curves of input operation, and equidistantly by Pload-t curve discretizations, obtain arbitrary
Bearing power Pload (k) under moment k；
The Pload (k) at kth moment is substituted into equation P fc (k)+Pbat (k)=Pload (k), and with Pfcmax, Pfcmin
For boundary constraint, all [Pfc, Pbat] distribution combinations for meeting power performance requirement are inscribed when can obtain this.
Further, the acquisition process of [Pfc, the Pbat] distribution combination, including step：
To obtain fuel cell, the lithium battery power allocation case of all meet demand power, by the Pload at kth moment
(k) substitute into equation P fc (k)+Pbat (k)=Pload (k), and using Pfcmax, Pfcmin as boundary constraint, using Pfc as horizontal axis,
Pbat is the longitudinal axis, establishes rectangular coordinate system, draws Pfc (k)+Pbat (k)=Pload (k), and take all feasible programs spare；
Above formula can turn to normal equation：Pbat (k)=- Pfc (k)+Pload (k), that is, the image drawn should be a slope
It is 1, intercept is the straight line of Pload (k)；
[Pfc, Pbat] distribution combination is obtained by the straight line.
Further, in the step S200, by satisfactory optimization, further accurate and feasible domain range, including step：
Extent function is established respectively, and acquisition is satisfied with angle value；
Durability comprehensive satisfaction function is established, satisfactory combination is obtained；
Obtain controlled feasible zone range.
Further, it is described establish extent function acquisition be satisfied with angle value, including step：Meet power performance by all
It is required that [Pfc, Pbat] distribution combination substitute into fuel cell output power change rate extent function δ 1 and charging and discharging lithium battery
It is calculated in electric current extent function δ 2, respectively obtains two and be satisfied with angle value.To characterize any time to fuel cell and lithium
The satisfaction of cell durability.
The method that " 0-1 " setting method can be taken to the setting of extent function or use piecewise function, to buffer satisfaction
Downward trend, avoid optimizing the possibility occurred without solution.
Further, in view of fuel battery power change rate and charging and discharging lithium battery high current are to the two durability
Negative effect, or even irreversible destruction can be carried out to Power supply belt, in setting fuel cell output power change rate satisfaction letter
When number δ 1 and charging and discharging lithium battery electric current extent function δ 2, it is the independent variable of function δ 1 to take (Pfc (k)-Pfc (k-1)) respectively,
It is the independent variable of function δ 2 to take Ibat (k), and function value scope limitation is within the scope of 0-1, to meet the substantially former of satisfactory optimization
Reason.
Further, the durability comprehensive satisfaction function of establishing obtains satisfactory combination, including step：Pass through tradeoff
To the degree that fuel cell durability and lithium battery durability stress, respectively to fuel cell output power change rate satisfaction letter
Number δ 1 and charging and discharging lithium battery electric current extent function δ 2 carries out linear weighted function, obtains comprehensive satisfaction function δ；And take comprehensive satisfaction
Degree functional value δ is combined as satisfactory combination at [Pfc1, the Pbat1] of μ or more, is otherwise considered as out of control.Adjust fuel cell output work
Rate improves its military service performance to promote its durability.
Further, the μ values 0.85-0.95.
Further, when the controlled feasible zone range of the acquisition, by meet comprehensive satisfaction requirement [Pfc1,
Pbat1] it combines and records, it is combined as meeting the parameter group of fuel cell durability and lithium battery durability as sharing of load
It closes, as controlled feasible zone range.
Further, in the step S300, optimized by optimum control, determine the optimum allocation of load power, wrapped
Include step：
Using the satisfactory combination as feasible zone, variable in order to control is arranged in fuel cell output power, by lithium battery SOC
It is set as state variable；
Offline power distribution is carried out to operating mode load using PMP algorithms based on the equivalent hydrogen consumption minimum principle of fuel cell；
Traversal optimizing in feasible zone is carried out to hamilton's function, it is final to obtain the optimal output reference power of fuel cell.
Using the advantageous effect of the technical program：
The present invention re-establishes Optimization Framework by introducing satisfactory optimization thought, and the satisfaction of each individual index is weighted,
Comprehensive satisfaction function is established, and as object function, can reflect the comprehensive benefit situation under different load power distribution；
According to optimization method step by step, available power distributes feasible zone, is then carried out using PMP methods in real-time control at any time
Load power is distributed, and PMP methods have the characteristics that real-time, easily realize, is simple；Utilize the finally obtained sharing of load of this method
Available more preferably vehicle economy, power source durability, can more scientific ground planning operation whole process operating mode situation；
The present invention can promote fuel cell durability, effectively extend fuel cell service life and performance；It is to keep away simultaneously
Exempt from undue weight fuel cell durability and may caused by economy bad problem fuel economy, i.e. hydrogen consumption are made
For the core and emphasis of optimization；
Satisfactory optimization thought is applied to fuel cell hybrid system optimization and control field by the present invention, constructs combustion
Expect cell hybrid power system muti-criteria satisfactory optimization model, it is proposed that the fuel cell output power variation based on durability
Rate/charging and discharging lithium battery electric current extent function；
The present invention is using priority from the progressive Optimization Framework of the low layering of height, wherein and high priority is system hard constraint,
It all must satisfy the constraint in any case, system could work normally, therefore high priority index will act on optimization process
Any moment；Middle priority is set as auxiliary Con trolling index, optimal to no longer being imposed in the optimization process of Two-stage control target
Solution, and with the thought of satisfactory optimization instead of optimal, to obtain broader feasible zone and control freedom degree；Low priority is main control
Index need to carry out PMP algorithm optimizing when optimization proceeds to the rank in the feasible zone that satisfactory optimization obtains, and it is negative to obtain this
Optimal power allocation under lotus state, it is ensured that the uniqueness of entire optimization solution.
Description of the drawings
Fig. 1 is a kind of flow diagram of fuel cell hybrid vehicle multiobjective optimization control method of the present invention；
Fig. 2 is the workload demand power against time curve of the embodiment of the present invention；
Fig. 3 is that all Pfc-Pbat for meeting load power demand of the embodiment of the present invention combine feasible zone.
Specific implementation mode
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made into one below in conjunction with the accompanying drawings
Step illustrates.
In the present embodiment, shown in Figure 1, it is excellent that the present invention proposes a kind of fuel cell hybrid vehicle multiple target
Change control method, including step：
S100 establishes all power-efficient domains of fuel cell hybrid vehicle；
S200 passes through satisfactory optimization, further accurate and feasible domain range；
S300 is optimized by optimum control, determines the optimum allocation of load power.
As the prioritization scheme of above-described embodiment, in the step S100, the complete of fuel cell hybrid vehicle is established
Body power-efficient domain, including step：
The whole actual measurement operating mode Pload-t curves of input operation, and equidistantly by Pload-t curve discretizations, obtain arbitrary
Bearing power Pload (k) under moment k；N discrete demand power points can be obtained；The n that is denoted as Pload (k), k=1,2 ....
To obtain fuel cell, the lithium battery power allocation case of all meet demand power, by the Pload at kth moment
(k) equation P fc (k)+Pbat (k)=Pload (k) is substituted into, and using Pfcmax, Pfcmin as boundary constraint, is inscribed when can obtain this
All [Pfc, Pbat] distribution combinations for meeting power performance requirement.
Wherein, the acquisition process of described [Pfc, Pbat] distribution combination, including step：
To obtain fuel cell, the lithium battery power allocation case of all meet demand power, by the Pload at kth moment
(k) substitute into equation P fc (k)+Pbat (k)=Pload (k), and using Pfcmax, Pfcmin as boundary constraint, using Pfc as horizontal axis,
Pbat is the longitudinal axis, establishes rectangular coordinate system, draws Pfc (k)+Pbat (k)=Pload (k), and take all feasible programs spare；
Above formula can turn to normal equation：Pbat (k)=- Pfc (k)+Pload (k), that is, the image drawn should be a slope
It is 1, intercept is the straight line of Pload (k)；
[Pfc, Pbat] distribution combination is obtained by the straight line.
Specific embodiment is：
The actual measurement operating mode Pload-t that takes typical operation whole as shown in Fig. 2, and by the Data Discretization on curve, every two
Time interval is 0.2s between a power points；The total 708s of target whole process, power points are 3540 total.
The DC bus-bar voltage of initialization system is 60V, and workload demand changed power amplitude is pressed about in -5kw between 7kw
Parameter matching is carried out to system according to the principle of degree of mixing 50%.Therefore select fuel cell rated power for 3kW, lithium battery capacity
For 30Ah.To avoid the frequent start-stop machine of fuel cell system, fuel cell minimum operating power is set as its rated power
10%, i.e. 300W reduce attenuation degree to protect fuel battery performance, and setting fuel cell peak power output is that its is specified
Power, i.e. 3kW.
To obtain fuel cell, the lithium battery power allocation case of all meet demand power, by the Pload at kth moment
(k) substitute into equation P fc (k)+Pbat (k)=Pload (k), and using Pfcmax, Pfcmin as boundary constraint, using Pfc as horizontal axis,
Pbat is the longitudinal axis, establishes rectangular coordinate system, draws Pfc (k)+Pbat (k)=Pload (k).
The present embodiment takes any point Pload=1823.1W herein, to [Pfc, the Pbat] of all meet demand power into
Row is found, as shown in figure 3, to illustrate above-mentioned image.
As the prioritization scheme of above-described embodiment, in the step S200, by satisfactory optimization, further accurate and feasible domain
Range, including step：
Extent function is established respectively, and acquisition is satisfied with angle value；
Durability comprehensive satisfaction function is established, satisfactory combination is obtained；
Obtain controlled feasible zone range.
As the prioritization scheme of above-described embodiment, it is described establish extent function acquisition be satisfied with angle value, including step：By institute
There is [Pfc, Pbat] distribution combination for meeting power performance requirement to substitute into fuel cell output power change rate extent function δ 1
It is calculated in charging and discharging lithium battery electric current extent function δ 2, respectively obtains two and be satisfied with angle value.To characterize any time
To the satisfaction of fuel cell and lithium battery durability.
The method that " 0-1 " setting method can be taken to the setting of extent function or use piecewise function, to buffer satisfaction
Downward trend, avoid optimizing the possibility occurred without solution.
Negative effect in view of fuel battery power change rate and charging and discharging lithium battery high current to the two durability, very
Can extremely irreversible destruction be carried out to Power supply belt, in setting fuel cell output power change rate extent function δ 1 and lithium battery
It is the independent variable of function δ 1 when charging and discharging currents extent function δ 2, to take (Pfc (k)-Pfc (k-1)) respectively, takes the Ibat (k) to be
The independent variable of function δ 2, function value scope limitation is within the scope of 0-1, to meet the basic principle of satisfactory optimization.
As the prioritization scheme of above-described embodiment, the durability comprehensive satisfaction function of establishing obtains satisfactory combination, packet
Include step：By weighing the degree stressed fuel cell durability and lithium battery durability, respectively to fuel cell output work
Rate change rate extent function δ 1 and charging and discharging lithium battery electric current extent function δ 2 carries out linear weighted function, obtains comprehensive satisfaction letter
Number δ；And comprehensive satisfaction functional value δ is taken to be combined as satisfactory combination at [Pfc1, the Pbat1] of μ or more, otherwise it is considered as out of control.It adjusts
Fuel cell output power is saved to promote its durability, improves its military service performance.
Wherein, the μ values 0.85-0.95.
To avoid optimization from occurring without the case where solution, the satisfied and out of control boundary of reduction, the multiobjective optimization control method is set
The extent function of meter uses fuzzy decision, and the satisfaction to buffer section out of control declines degree.
As the prioritization scheme of above-described embodiment, when the controlled feasible zone range of the acquisition, comprehensive satisfaction will be met
[Pfc1, Pbat1] combination that degree requires is recorded, and is combined as meeting fuel cell durability and lithium battery as sharing of load
The parameter combination of durability, as controlled feasible zone range.
Specific embodiment：
The fuel cell rated output power used sets 400W/s for 3kW, and 600W/s is relatively satisfactory region and dead band
Domain turning point, 1500W/s are limiting value.Therefore 1 expression formulas of fuel cell output power change rate extent function δ are：
This patent is by investigating satisfaction height of the lithium battery output current value with indirect measure to lithium battery durability.
The fuel cell rated output power that the present embodiment uses is 30Ah, and 15A is as region turning point out of control, 45A when setting charging
Limiting value；Set electric discharge when 75A as region turning point out of control, 120A be limiting value.The corresponding current value of charge and discharge is taken to be respectively
It is negative, positive, therefore 2 expression formulas of charging and discharging lithium battery electric current extent function δ are：
This patent set comprehensive extent function γ is：
In above formula, the control targe of priority has reached desired comprehensive satisfaction during γ=1 is indicated, i.e., corresponding at this time
Power distribution result [Pfc, Pbat] should retain, conversely, the control targe of priority is not up to desired in being indicated if γ=0
Comprehensive satisfaction, i.e., corresponding power distribution result [Pfc, Pbat] should be given up at this time.
As the prioritization scheme of above-described embodiment, in the step S300, is optimized by optimum control, determine load power
Optimum allocation, including step：
Using the satisfactory combination as feasible zone, variable in order to control is arranged in fuel cell output power, by lithium battery SOC
It is set as state variable；
Offline power distribution is carried out to operating mode load using PMP algorithms based on the equivalent hydrogen consumption minimum principle of fuel cell；
Traversal optimizing in feasible zone is carried out to hamilton's function, it is final to obtain the optimal output reference power of fuel cell.
Specific implementation process：Fuel cell hybrid system is by fuel cell and lithium battery group at the hybrid power system
The energy management strategies of system can be reduced to the control problem of single-degree-of-freedom, i.e., using the output power of fuel cell as the control of system
Variable u (t), the state-of-charge (State of charge, SOC) of battery is the state variable x (t) of system, with fuel cell
Fuel consumption mesh in order to control：
CH2:The instantaneous hydrogen consumption of fuel cell is represented, value is generally directly proportional to the output power of fuel cell, i.e.,
CH2=aPfc+b；
tf：Represent end time.
The state equation of system is：
In above formula, Ibat represents battery charging and discharging electric current, and respectively represents electric discharge and charging process with positive and negative, and Pbat is lithium
Battery charging and discharging power, Rint are battery charging and discharging internal resistance, and generally at identical SOC, charge and discharge internal resistance is not quite similar, and Voc is
The open-circuit voltage of battery.
According to Pang Te lia king minimal principles, for no constraint that is converted into the problem of seeking system minimum of belt restraining is asked
Topic, need to build hamilton's function.
The general type of hamilton's function is：
H (x, u, λ, t)=L (x, u, t)+λ f (x, u, t)；
In the system, hamilton's function is：
The required per optimal output power of moment fuel cell taken, can be obtained, i.e., by seeking the minimum of above formula
In above formula, variable allows the reachable set to get to feasible zone range to R in order to control.
After obtaining Pfc-opt (k), subtract each other with Pload (k), i.e. the optimal output power of fuel cell and workload demand work(
The power that the difference portion of rate, as lithium battery should export, is denoted as Pbat-opt (k).
So far the Optimal Load power distribution per any time can be obtained.
The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention.The technology of the industry
Personnel are it should be appreciated that the present invention is not limited to the above embodiments, and the above embodiments and description only describe this
The principle of invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these changes
Change and improvement all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and its
Equivalent thereof.
Claims (10)
1. a kind of fuel cell hybrid vehicle multiobjective optimization control method, which is characterized in that including step：
S100 establishes all power-efficient domains of fuel cell hybrid vehicle；
S200 passes through satisfactory optimization, further accurate and feasible domain range；
S300 is optimized by optimum control, determines the optimum allocation of load power.
2. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 1, feature exist
In in the step S100, establishing all power-efficient domains of fuel cell hybrid vehicle, including step：
The whole actual measurement operating mode Pload-t curves of input operation, and Pload-t curve discretizations are equidistantly obtained into any time k
Under bearing power Pload (k)；
The Pload (k) at kth moment is substituted into equation P fc (k)+Pbat (k)=Pload (k), and using Pfcmax, Pfcmin as side
Bound constrained inscribes all [Pfc, Pbat] distribution combinations for meeting power performance requirement when can obtain this.
3. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 2, feature exist
In, the acquisition process of [Pfc, the Pbat] distribution combination, including step：
The Pload (k) at kth moment is substituted into equation P fc (k)+Pbat (k)=Pload (k), and using Pfcmax, Pfcmin as side
Bound constrained is the longitudinal axis by horizontal axis, Pbat of Pfc, establishes rectangular coordinate system, draws Pfc (k)+Pbat (k)=Pload (k), and
Take all feasible programs spare；
Above formula can turn to normal equation：Pbat (k)=- Pfc (k)+Pload (k), that is, it is 1 that the image drawn, which should be a slope,
Intercept is the straight line of Pload (k)；
[Pfc, Pbat] distribution combination is obtained by the straight line.
4. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 1, feature exist
In, in the step S200, by satisfactory optimization, further accurate and feasible domain range, including step：
Extent function is established respectively, and acquisition is satisfied with angle value；
Durability comprehensive satisfaction function is established, satisfactory combination is obtained；
Obtain controlled feasible zone range.
5. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 4, feature exist
In, it is described establish extent function acquisition be satisfied with angle value, including step：By all [Pfc, Pbat] for meeting power performance requirement
Distribution combination substitutes into fuel cell output power change rate extent function δ 1 and charging and discharging lithium battery electric current extent function δ 2
In calculated, respectively obtain two and be satisfied with angle value.
6. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 5, feature exist
In, when fuel cell output power change rate extent function δ 1 and charging and discharging lithium battery electric current extent function δ 2 is arranged,
It is the independent variable of function δ 1 to take (Pfc (k)-Pfc (k-1)) respectively, and it is the independent variable of function δ 2, function value model to take Ibat (k)
It encloses and is limited within the scope of 0-1.
7. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 6, feature exist
In the durability comprehensive satisfaction function of establishing obtains satisfactory combination, including step：By weighing to fuel cell durability
The degree stressed with lithium battery durability, respectively to fuel cell output power change rate extent function δ 1 and lithium battery charge and discharge
Electric current extent function δ 2 carries out linear weighted function, obtains comprehensive satisfaction function δ；And take comprehensive satisfaction functional value δ in μ or more
[Pfc1, Pbat1] be combined as satisfactory combination, be otherwise considered as out of control.
8. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 7, feature exist
In the μ values 0.85-0.95.
9. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 8, feature exist
When, the acquisition controlled feasible zone range, [Pfc1, the Pbat1] that meets comprehensive satisfaction requirement combination is recorded
Come, is combined as meeting the parameter combination of fuel cell durability and lithium battery durability as sharing of load, it is as controlled
Feasible zone range.
10. a kind of fuel cell hybrid vehicle multiobjective optimization control method according to claim 1, feature exist
In in the step S300, being optimized by optimum control, determine the optimum allocation of load power, including step：
Using the satisfactory combination as feasible zone, variable in order to control is arranged in fuel cell output power, lithium battery SOC is arranged
For state variable；
Offline power distribution is carried out to operating mode load using PMP algorithms based on the equivalent hydrogen consumption minimum principle of fuel cell；To breathing out
Milton function carries out the traversal optimizing in feasible zone, final to obtain the optimal output reference power of fuel cell.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810214850.XA CN108437822B (en) | 2018-03-15 | 2018-03-15 | A kind of fuel cell hybrid vehicle multiobjective optimization control method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810214850.XA CN108437822B (en) | 2018-03-15 | 2018-03-15 | A kind of fuel cell hybrid vehicle multiobjective optimization control method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108437822A true CN108437822A (en) | 2018-08-24 |
| CN108437822B CN108437822B (en) | 2019-10-25 |
Family
ID=63194490
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810214850.XA Active CN108437822B (en) | 2018-03-15 | 2018-03-15 | A kind of fuel cell hybrid vehicle multiobjective optimization control method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108437822B (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110126679A (en) * | 2019-05-15 | 2019-08-16 | 福州大学 | A kind of acquisition methods of fuel cell best operating point |
| CN110135632A (en) * | 2019-04-29 | 2019-08-16 | 吉林大学 | PHEV adaptive optimal energy management method based on routing information |
| CN110194177A (en) * | 2019-06-28 | 2019-09-03 | 重庆大学 | A kind of plug-in hybrid passenger car energy management method based on the adaptive equivalent minimum strategy of consumption |
| CN110641456A (en) * | 2019-10-29 | 2020-01-03 | 重庆大学 | Plug-in hybrid power system two-state self-adaptive control method based on PMP principle |
| CN110962684A (en) * | 2019-11-15 | 2020-04-07 | 东华大学 | Electric automobile energy management and distribution method |
| CN111775775A (en) * | 2020-07-03 | 2020-10-16 | 广东国鸿氢能科技有限公司 | Fuel cell power control method when vehicle is in high-power demand working condition |
| CN112918330A (en) * | 2021-03-18 | 2021-06-08 | 北京交通大学 | Method for calculating optimal working state control strategy of fuel cell vehicle |
| CN113043917A (en) * | 2021-04-15 | 2021-06-29 | 西南交通大学 | Layered control method for multi-stack fuel cell hybrid power system |
| CN113085666A (en) * | 2021-05-18 | 2021-07-09 | 北京理工大学 | Energy-saving driving method for layered fuel cell automobile |
| CN114335632A (en) * | 2022-03-15 | 2022-04-12 | 青岛大学 | Two-layer real-time efficiency optimization method for heavy-load fuel cell hybrid power system |
| CN114889498A (en) * | 2022-05-07 | 2022-08-12 | 苏州市华昌能源科技有限公司 | Power optimization distribution method of hydrogen-electricity hybrid power system |
| CN117922384A (en) * | 2024-03-25 | 2024-04-26 | 山东大学 | Energy control method and system for fuel cell hybrid power pure electric ship |
| CN117922384B (en) * | 2024-03-25 | 2024-06-04 | 山东大学 | Energy control method and system for fuel cell hybrid power pure electric ship |
-
2018
- 2018-03-15 CN CN201810214850.XA patent/CN108437822B/en active Active
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110135632A (en) * | 2019-04-29 | 2019-08-16 | 吉林大学 | PHEV adaptive optimal energy management method based on routing information |
| CN110135632B (en) * | 2019-04-29 | 2022-11-25 | 吉林大学 | PHEV self-adaptive optimal energy management method based on path information |
| CN110126679B (en) * | 2019-05-15 | 2022-03-08 | 福州大学 | Method for acquiring optimal operating point of fuel cell |
| CN110126679A (en) * | 2019-05-15 | 2019-08-16 | 福州大学 | A kind of acquisition methods of fuel cell best operating point |
| CN110194177A (en) * | 2019-06-28 | 2019-09-03 | 重庆大学 | A kind of plug-in hybrid passenger car energy management method based on the adaptive equivalent minimum strategy of consumption |
| CN110641456A (en) * | 2019-10-29 | 2020-01-03 | 重庆大学 | Plug-in hybrid power system two-state self-adaptive control method based on PMP principle |
| CN110962684A (en) * | 2019-11-15 | 2020-04-07 | 东华大学 | Electric automobile energy management and distribution method |
| CN111775775A (en) * | 2020-07-03 | 2020-10-16 | 广东国鸿氢能科技有限公司 | Fuel cell power control method when vehicle is in high-power demand working condition |
| CN112918330A (en) * | 2021-03-18 | 2021-06-08 | 北京交通大学 | Method for calculating optimal working state control strategy of fuel cell vehicle |
| CN113043917B (en) * | 2021-04-15 | 2022-09-02 | 西南交通大学 | Layered control method for multi-stack fuel cell hybrid power system |
| CN113043917A (en) * | 2021-04-15 | 2021-06-29 | 西南交通大学 | Layered control method for multi-stack fuel cell hybrid power system |
| CN113085666A (en) * | 2021-05-18 | 2021-07-09 | 北京理工大学 | Energy-saving driving method for layered fuel cell automobile |
| WO2022241898A1 (en) * | 2021-05-18 | 2022-11-24 | 北京理工大学 | Hierarchical energy-saving driving method for fuel cell vehicle |
| CN114335632A (en) * | 2022-03-15 | 2022-04-12 | 青岛大学 | Two-layer real-time efficiency optimization method for heavy-load fuel cell hybrid power system |
| CN114889498A (en) * | 2022-05-07 | 2022-08-12 | 苏州市华昌能源科技有限公司 | Power optimization distribution method of hydrogen-electricity hybrid power system |
| CN114889498B (en) * | 2022-05-07 | 2023-12-15 | 苏州市华昌能源科技有限公司 | Power optimization distribution method of hydrogen-electricity hybrid power system |
| CN117922384A (en) * | 2024-03-25 | 2024-04-26 | 山东大学 | Energy control method and system for fuel cell hybrid power pure electric ship |
| CN117922384B (en) * | 2024-03-25 | 2024-06-04 | 山东大学 | Energy control method and system for fuel cell hybrid power pure electric ship |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108437822B (en) | 2019-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108437822B (en) | A kind of fuel cell hybrid vehicle multiobjective optimization control method | |
| Etxeberria et al. | Hybrid energy storage systems for renewable energy sources integration in microgrids: A review | |
| Tao et al. | Orderly charging strategy of battery electric vehicle driven by real-world driving data | |
| Tao et al. | Frequency decoupling-based energy management strategy for fuel cell/battery/ultracapacitor hybrid vehicle using fuzzy control method | |
| Zhao et al. | Energy storage system for a port crane hybrid power-train | |
| Kermani et al. | A comprehensive technoeconomic solution for demand control in ports: Energy storage systems integration | |
| Gao et al. | Adaptive real-time optimal energy management strategy based on equivalent factors optimization for hybrid fuel cell system | |
| CN108011437A (en) | Hybrid energy-storing power distribution system and method with super-charge super-discharge protective device | |
| CN110549914B (en) | Approximate optimal energy management method for daily operation of fuel cell tramcar | |
| Bahloul et al. | Impact of power sharing method on battery life extension in HESS for grid ancillary services | |
| Huangfu et al. | An Improved Energy Management Strategy for Fuel Cell Hybrid Vehicles Based on Pontryagin's Minimum Principle | |
| CN106505604B (en) | The photovoltaic energy storage combined operating unit optimization configuration method of access area power distribution network | |
| DE102013214287A1 (en) | Control device for electrical energy | |
| Lin et al. | Predictive-ECMS based degradation protective control strategy for a fuel cell hybrid electric vehicle considering uphill condition | |
| Le Duigou et al. | On the comparison and the complementarity of batteries and fuel cells for electric driving | |
| Herrera et al. | Adaptive and non-adaptive strategies for optimal energy management and sizing of a dual storage system in a hybrid electric bus | |
| CN107124002A (en) | Independent micro-grid energy optimal control method based on bi-fuzzy control | |
| CN112434377B (en) | Parameter design and evaluation method for locomotive power battery pack | |
| Zhang et al. | Comparison of daily operation strategies for a fuel cell/battery tram | |
| Almehizia et al. | Investigation of V2G economical viability | |
| CN114889498A (en) | Power optimization distribution method of hydrogen-electricity hybrid power system | |
| CN116454879A (en) | Virtual power plant type railway traction power supply system and control method | |
| CN111106612A (en) | Energy storage type charging pile participating power grid demand side response combined operation optimization model and solving algorithm | |
| Traoré et al. | Energy management strategy design based on frequency separation, fuzzy logic and Lyapunov control for multi-sources electric vehicles | |
| Vlahopoulos et al. | Solution for RTG crane power supply with the use of a hybrid energy storage system based on literature review |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |