CN112670539A - Method for accurately obtaining relation between output current and output power of fuel cell system - Google Patents
Method for accurately obtaining relation between output current and output power of fuel cell system Download PDFInfo
- Publication number
- CN112670539A CN112670539A CN202011535832.5A CN202011535832A CN112670539A CN 112670539 A CN112670539 A CN 112670539A CN 202011535832 A CN202011535832 A CN 202011535832A CN 112670539 A CN112670539 A CN 112670539A
- Authority
- CN
- China
- Prior art keywords
- value
- fuel cell
- output power
- output current
- curve
- 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.)
- Pending
Links
- 239000000446 fuel Substances 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 35
- 239000002245 particle Substances 0.000 claims abstract description 36
- 238000002474 experimental method Methods 0.000 claims abstract description 16
- 238000004088 simulation Methods 0.000 claims abstract description 16
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 7
- 230000006870 function Effects 0.000 claims description 32
- 238000005457 optimization Methods 0.000 claims description 8
- 239000012528 membrane Substances 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 5
- 230000003044 adaptive effect Effects 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 2
- 238000004364 calculation method Methods 0.000 abstract description 2
- 239000001301 oxygen Substances 0.000 description 4
- 229910052760 oxygen Inorganic materials 0.000 description 4
- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 description 3
- QVGXLLKOCUKJST-UHFFFAOYSA-N atomic oxygen Chemical compound [O] QVGXLLKOCUKJST-UHFFFAOYSA-N 0.000 description 3
- 239000001257 hydrogen Substances 0.000 description 3
- 229910052739 hydrogen Inorganic materials 0.000 description 3
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000033228 biological regulation Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 239000007795 chemical reaction product Substances 0.000 description 1
- 230000007423 decrease Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003487 electrochemical reaction Methods 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 239000000376 reactant Substances 0.000 description 1
- 230000036632 reaction speed Effects 0.000 description 1
- 238000002922 simulated annealing Methods 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
Images
Classifications
-
- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/50—Fuel cells
Landscapes
- Fuel Cell (AREA)
Abstract
The invention discloses a method for accurately obtaining the relation between the output current and the output power of a fuel cell system. Selecting a proper curve function equation to be fitted according to an output current-output power characteristic curve obtained by a calibration experiment; then combining experiments and simulink fuel cell modeling simulation to obtain output current and output power values; and performing parameter solving on the output current and the output power by using a particle swarm algorithm to obtain a relation curve of the output current and the output power of the fuel cell system. The invention has the advantages that: the reliability of data and models is improved, the output current and the output power value under different working conditions can be obtained by changing experimental conditions in simulation, the method is used for multi-working-condition research, the adaptability is improved, and the research cost is reduced. And solving parameters of the curve to be fitted by adopting a particle swarm algorithm, so that the calculation efficiency and the curve precision are improved, and the accuracy of controlling the output power by utilizing the curve is further improved.
Description
Technical Field
The invention relates to the technical field of fuel cell vehicle control, in particular to a method for accurately obtaining the relation between the output current and the output power of a fuel cell system.
Background
The problems of environmental pollution and energy shortage are increasingly serious in the global scope, energy conservation and environmental protection are also highly regarded as important in the automobile industry, and new energy automobiles are produced and rapidly developed. The hydrogen-oxygen fuel cell converts chemical energy of fuel hydrogen and oxygen into electric energy in an electrochemical reaction mode, and is widely applied to an automobile power system due to the advantages of high reaction efficiency and no pollution of reaction products.
In order to reasonably distribute power among different power sources according to the running conditions of the vehicle, a DC-DC converter and a fuel cell are generally connected into a bus together, so that a fuel cell system is in a reasonable power range, and the service life and the working efficiency of parts of a power system are ensured. In a DC-DC converter control system, a fuel cell output current-output power characteristic curve is generally read for power regulation, and therefore, obtaining an accurate characteristic curve is an important issue for a fuel cell control system.
In order to solve the above problems, various solutions have been proposed by scholars at home and abroad. The chinese patent with application publication No. CN110774942 discloses a method for controlling output power of a fuel cell in a hybrid power drive system, which calibrates the maximum power point of the fuel cell based on a simulated annealing method, and improves the accuracy. However, the method is easy to fall into local optimization for the multi-parameter fitting problem.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a method for establishing a fluorescent oil film gray scale and thickness model based on a neural network, and solves the defects in the prior art.
In order to realize the purpose, the technical scheme adopted by the invention is as follows:
a method of accurately deriving a fuel cell system output current versus output power relationship, comprising the steps of:
the method comprises the following steps: selecting a proper curve function equation to be fitted according to a fuel cell system output current-output power characteristic curve obtained by a calibration experiment;
step two: establishing a proton exchange membrane fuel cell model by combining experiments and simulink on the fuel cell to obtain an output current value and an output power value of a fuel cell system; the output current and the output power of the fuel cell are obtained by combining experiments and simulink modeling simulation, the accuracy of experimental data can be ensured, and the experimental cost and the time cost are reduced. The experimental results are used as a basic database to verify the accuracy of the simulink model. In addition, the simulation working condition is changed by changing the parameter setting in the model, and the adaptability of the method is improved.
Step three: and searching a parameter optimal solution in the curve function to be fitted by adopting a Particle Swarm Optimization (PSO) algorithm according to the obtained output current value and output power value.
Further, in the first step, a polynomial fitting method and mathematical experience are combined to determine a suitable curve functional relation to be fitted.
Further, in the second step, the fuel cell is tested, the current density is changed for loading and load reduction, and the output current value and the output power value in the power increasing and power reducing processes are obtained.
A proton exchange membrane fuel cell model is established in simulink, and the current value and the power value under different working conditions are obtained by changing the temperature and the air inlet pressure to change the simulation working conditions, so that the method disclosed by the invention is suitable for curve fitting of various working conditions, and the adaptability of the method is improved.
Further, the Particle Swarm Optimization (PSO) in step three, which obtains the optimal solution of the parameter with the fitting function, includes the following specific steps:
step 201: initializing a solution space;
step 202: calculating an adaptive function value f according to the experimental data obtained in the step two0Obtaining the individual extremum and population of the particlesThe optimal position of the game;
step 203: according to the update speed v according to the formulas (1-1) and (1-2)1And position x1；
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid) (1-1)
Xid＝Xid+Vid (1-2)
Wherein, VidIs the particle velocity, XidAs the current position of the particle, PidIs an individual extremum, PgdIs a global extremum.
Omega is an inertial weight factor, and C1 and C2 are an individual learning factor and a global learning factor respectively.
Step 204: calculating a fitness function value f of the particle′Comparison of f′And f0。
Step 205: judging whether the iteration times meet the iteration time value set in Step201, if so, performing Step S206, otherwise, performing Step S203;
step 206: and updating the fitness function value, and outputting the corresponding parameter value of the curve to be fitted when the fitness function value is minimum.
Further, the fitness function is:
wherein F represents the value of the fitted curve function, yiThe true value is represented, n represents the number of data used for fitting, and e represents the current parameter.
Further, if f'<f0Let f0F' and updating the individual extremum P of the particlesid＝x1In this case, the global optimum position is the position P where the particle having the smallest fitness function value is locatedgd＝xfmin；
Compared with the prior art, the invention has the advantages that:
the method obtains the output current and the output power of the fuel cell through experiments, ensures the reliability of data, and combines simulink to build a proton exchange membrane fuel cell model for simulation, so that the experimental conditions can be changed to obtain the output current and the output power value under different working conditions, and the method is suitable for multi-working condition research. In addition, the data obtained by the experiment can be used for checking the accuracy of the simulation model, and the data and the accuracy are combined, so that the experiment cost and the time cost are reduced.
The Particle Swarm Optimization (PSO) is adopted to fit the obtained current value and power value, so that the problem of local optimum under the condition of a plurality of variables is effectively avoided, the convergence is better, a more accurate output current-output power characteristic curve of the fuel cell system is obtained, and the accuracy of controlling the output power by utilizing the curve is further improved.
Drawings
FIG. 1 is a block diagram of the architecture of the method of an embodiment of the present invention;
FIG. 2 is a flow chart of a method of an embodiment of the present invention;
FIG. 3 is a graph of output current-output power characteristics of a fuel cell obtained from a calibration experiment according to an embodiment of the present invention;
FIG. 4 is a graph of output current versus output power characteristics of a fuel cell fitted to experimental test data according to an embodiment of the present invention;
fig. 5 is a graph of output current versus output power characteristics of a fuel cell fitted to simulink simulation data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail below with reference to the accompanying drawings by way of examples.
As shown in fig. 1 and 2, the present invention provides a method for accurately obtaining the output current and output power relationship of a fuel cell system, which comprises the following steps:
the method comprises the following steps: selecting a proper curve function equation to be fitted based on a polynomial fitting method and combined with experience according to a fuel cell system output current-output power characteristic curve obtained by a calibration experiment;
in this embodiment, a nearly linear relationship between the output current and the output power obtained by the calibration experiment is shown in fig. 3, and a function of a curve to be fitted is empirically selected as:
y＝ax3+bx2+cx+d
wherein y represents the output power value, x represents the output current value, and a, b, c and d are parameters to be fitted.
Step two: combining experiments and simulink modeling simulation to obtain the output current value and the output power value of the fuel cell system;
in this embodiment, the fuel cell stack with a rated power of 62kW is selected for the experiment, and the fuel cell stack is subjected to the pressure rise and pressure drop experiment by changing the current density, so as to ensure the reaction temperature and environment of the hydrogen and oxygen reactants, and the flow rate of the circulating water is appropriately adjusted according to the temperature of the hydrogen and oxygen in and out of the stack during the pressure rise process.
Taking the process that the output current of the electric pile is increased from 211A to 326A, and the output power is increased from 42kW to 62kW as an example, in the process, the circulating water flow is adjusted from 70kg/s to 150kg/s so as to ensure that the voltage of a single fuel cell is stable, and the temperature difference between the inlet and the outlet of the fuel is within 10 degrees.
In this embodiment, a proton exchange membrane fuel cell model is established in simulink to perform simulation, and simulation under different working conditions is realized by changing the fuel inlet pressure.
In this embodiment, in the simulink simulation model of the fuel cell stack, the reaction speed may be increased by increasing the pressure of fuel entering the stack, and at this time, the voltage decreases, the current increases, and the current variation value is greater than the voltage variation value. Therefore, the output power increases according to P ═ I × V.
In the present embodiment, the fuel cell simulation is performed under pressures of 1atm, 2atm, and 3atm, respectively, to obtain the output current and the output power value, the relation between the output current and the output power can be changed by changing the stack-entering pressure, and when the output current is 103A, the output powers corresponding to the pressures of 1atm, 2atm, and 3atm are 21.25kW, 22.65kW, and 23.65kW, respectively.
Step three: and searching a parameter optimal solution in the curve function to be fitted by adopting a Particle Swarm Optimization (PSO) algorithm according to the obtained output current value and output power value.
In the embodiment, a Particle Swarm Optimization (PSO) algorithm is adopted to fit the multi-parameter curve, so that the probability of local optimum can be reduced, and the reliability of the fit parameters can be improved.
The particle swarm algorithm comprises the following specific calculation steps:
step 201: initializing a solution space, and setting the particle size, the particle dimension, the maximum speed setting of each dimension of the particle and the maximum iteration times of the particle;
in this example, the particle size M is set to 300, the particle dimension n to 4, and the maximum velocity V is set according to the function of the selected curve to be fittedmax1, maximum number of iterations Tmax＝1000。
Step 202: randomly initializing the velocity v of each solution in a particle swarm search space0And position x0Calculating an adaptive function value f0Obtaining the individual extreme value of the particle and the global optimal position of the group, Pid＝x0The global extreme value is equal to the individual extreme value;
the particle velocity and position update formulas are respectively:
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid) (1-1)
Xid＝Xid+Vid (1-2)
wherein, VidIs the particle velocity, XidAs the current position of the particle, PidIs an individual extremum, PgdIs a global extremum.
Omega is an inertial weight factor, and C1 and C2 are an individual learning factor and a global learning factor respectively.
In this embodiment, the inertial weight factor, the individual learning factor, and the global learning factor are set to have a classical setting ω of 0.729 and c1 of c2 of 1.4962.
The way of verifying the fitting effect is as follows: substituting current parameter value into power value PrObtaining a current value IeCalculating the current value and the work in experimental and simulation dataRate PrCorresponding current value IrThe deviation therebetween. In this example, therefore, the selected fitness function is:
wherein e represents the parameters a, b, c, d.
Step 203: each particle is based on its own individual extremum and global optimum solution, according to equations (1-1) and (1-2) of claim 3, on the update speed v1And position x1；
The fitness function represents x under the current fitting parametersiCorresponding fiValue (i.e. power value P)rCorresponding fitting current value Ie) And reality yiValue (i.e. this power value P)rCorresponding actual current value Ir) And so a smaller fitness function value indicates a higher individual fitness.
Step 204: calculating a fitness function value f of the particle′Comparison of f′And f0If f is′<f0Let f0＝f′And updating individual extreme value P of the particleid＝x1In this case, the global optimum position is the position P where the particle having the smallest fitness function value is locatedgd＝xfmin；
Step 205: judging whether the iteration times meet the maximum iteration time value set in Step201, if so, performing Step S206, otherwise, performing Step S203;
step 206: and outputting four corresponding parameter values when the fitness function value is minimum.
In this example, a fuel cell output current-output power characteristic curve obtained by fitting experimental test data is shown in fig. 4.
In this embodiment, the fuel cell simulation was performed under pressures of 1atm, 2atm, and 3atm, the output current and the output power value were obtained, and the output current-output power characteristic curve of the fuel cell obtained by fitting is shown in fig. 5.
It will be appreciated by those of ordinary skill in the art that the examples described herein are intended to assist the reader in understanding the manner in which the invention is practiced, and it is to be understood that the scope of the invention is not limited to such specifically recited statements and examples. Those skilled in the art can make various other specific changes and combinations based on the teachings of the present invention without departing from the spirit of the invention, and these changes and combinations are within the scope of the invention.
Claims (6)
1. A method for accurately obtaining a relationship between an output current and an output power of a fuel cell system, comprising the steps of:
the method comprises the following steps: selecting a proper curve function equation to be fitted according to a fuel cell system output current-output power characteristic curve obtained by a calibration experiment;
step two: establishing a proton exchange membrane fuel cell model by combining experiments and simulink on the fuel cell to obtain an output current value and an output power value of a fuel cell system;
step three: and searching a parameter optimal solution in the curve function to be fitted by adopting a Particle Swarm Optimization (PSO) algorithm according to the obtained output current value and output power value.
2. The method of claim 1, wherein: and in the first step, a proper curve function relation to be fitted is determined by combining a polynomial fitting method and mathematical experience.
3. The method of claim 2, wherein: in the second step, the fuel cell is tested, the current density is changed for loading and load reduction, and the output current value and the output power value in the power increasing and power reducing processes are obtained;
a proton exchange membrane fuel cell model is established in simulink, and the current value and the power value under different working conditions are obtained by changing the simulation working conditions through changing the temperature and the air inlet pressure.
4. The method of claim 3, wherein: the Particle Swarm Optimization (PSO) in the third step obtains the optimal solution of the parameters with the fitting function, and comprises the following specific steps:
step 201: initializing a solution space;
step 202: calculating an adaptive function value f according to the experimental data obtained in the step two0Obtaining the individual extreme value of the particle and the global optimal position of the group;
step 203: according to the update speed v according to the formulas (1-1) and (1-2)1And position x1；
Vid＝ωVid+C1random(0,1)(Pid-Xid)+C2random(0,1)(Pgd-Xid) (1-1)
Xid＝Xid+Vid (1-2)
Wherein, VidIs the particle velocity, XidAs the current position of the particle, PidIs an individual extremum, PgdRandom represents a random number, which is a global extremum.
Omega is an inertial weight factor, C1、C2Individual and global learning factors, respectively.
Step 204: calculating a fitness function value f 'of the particle, comparing f' and f0；
Step 205: judging whether the iteration times meet the iteration time value set in Step201, if so, performing Step S206, otherwise, performing Step S203;
step 206: and updating the fitness function value, and outputting the corresponding parameter value of the curve to be fitted when the fitness function value is minimum.
5. The method of claim 4, wherein: the fitness function is:
wherein F represents the value of the fitted curve function, yiRepresenting the true value, n representing the number used for the fitThe number of data, e, represents the current parameter.
6. The method of claim 5, wherein: if'<f0Let f0F' and updating the individual extremum P of the particlesid＝x1In this case, the global optimum position is the position P where the particle having the smallest fitness function value is locatedgd＝xfmin。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011535832.5A CN112670539A (en) | 2020-12-23 | 2020-12-23 | Method for accurately obtaining relation between output current and output power of fuel cell system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011535832.5A CN112670539A (en) | 2020-12-23 | 2020-12-23 | Method for accurately obtaining relation between output current and output power of fuel cell system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112670539A true CN112670539A (en) | 2021-04-16 |
Family
ID=75408010
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011535832.5A Pending CN112670539A (en) | 2020-12-23 | 2020-12-23 | Method for accurately obtaining relation between output current and output power of fuel cell system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112670539A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113555593A (en) * | 2021-06-04 | 2021-10-26 | 西南交通大学 | Proton exchange membrane fuel cell operation method based on physical field distribution heterogeneity |
| CN113659178A (en) * | 2021-07-28 | 2021-11-16 | 中车青岛四方机车车辆股份有限公司 | Multi-stack fuel cell power generation system coordinated control method and system and vehicle |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102195052A (en) * | 2010-03-15 | 2011-09-21 | 通用汽车环球科技运作有限责任公司 | Adaptive method for conversion of external power request to current setpoint to a fuel cell system based on stack performance |
| CN103384014A (en) * | 2013-05-29 | 2013-11-06 | 西南交通大学 | Maximum net power strategy based proton exchange membrane fuel cell air-supply system control |
| CN106709131A (en) * | 2016-11-15 | 2017-05-24 | 上海电机学院 | Parameter intelligent optimization method suitable for proton exchange membrane fuel cell model |
| CN111342086A (en) * | 2020-02-29 | 2020-06-26 | 同济大学 | Fuel cell air oxygen ratio and flow pressure cooperative control method and system |
| CN112001092A (en) * | 2020-09-01 | 2020-11-27 | 中国计量大学 | PEMFC operating condition optimization method for different power outputs |
-
2020
- 2020-12-23 CN CN202011535832.5A patent/CN112670539A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102195052A (en) * | 2010-03-15 | 2011-09-21 | 通用汽车环球科技运作有限责任公司 | Adaptive method for conversion of external power request to current setpoint to a fuel cell system based on stack performance |
| CN103384014A (en) * | 2013-05-29 | 2013-11-06 | 西南交通大学 | Maximum net power strategy based proton exchange membrane fuel cell air-supply system control |
| CN106709131A (en) * | 2016-11-15 | 2017-05-24 | 上海电机学院 | Parameter intelligent optimization method suitable for proton exchange membrane fuel cell model |
| CN111342086A (en) * | 2020-02-29 | 2020-06-26 | 同济大学 | Fuel cell air oxygen ratio and flow pressure cooperative control method and system |
| CN112001092A (en) * | 2020-09-01 | 2020-11-27 | 中国计量大学 | PEMFC operating condition optimization method for different power outputs |
Non-Patent Citations (2)
| Title |
|---|
| D. CHU等: "Analysis of PEM fuel cell stacks using an empirical current-voltage equation", 《JOURNAL OF APPLIED ELECTROCHEMISTRY》 * |
| MEIYING YE等: "Parameter identification for proton exchange membrane fuel cell model using particle swarm optimization", 《INTERNATIONAL JOURNAL OF HYDROGEN ENERGY》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113555593A (en) * | 2021-06-04 | 2021-10-26 | 西南交通大学 | Proton exchange membrane fuel cell operation method based on physical field distribution heterogeneity |
| CN113659178A (en) * | 2021-07-28 | 2021-11-16 | 中车青岛四方机车车辆股份有限公司 | Multi-stack fuel cell power generation system coordinated control method and system and vehicle |
| CN113659178B (en) * | 2021-07-28 | 2022-12-13 | 中车青岛四方机车车辆股份有限公司 | Multi-stack fuel cell power generation system coordinated control method and system and vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | A modified MPC-based optimal strategy of power management for fuel cell hybrid vehicles | |
| CN111129548B (en) | Improved particle swarm optimization fuzzy PID fuel cell temperature control method | |
| Caux et al. | On-line fuzzy energy management for hybrid fuel cell systems | |
| Pourkiaei et al. | Modeling and experimental verification of a 25W fabricated PEM fuel cell by parametric and GMDH-type neural network | |
| Ming et al. | A systematic review of machine learning methods applied to fuel cells in performance evaluation, durability prediction, and application monitoring | |
| CN112670539A (en) | Method for accurately obtaining relation between output current and output power of fuel cell system | |
| Lee et al. | Energy management strategy of fuel cell electric vehicles using model-based reinforcement learning with data-driven model update | |
| Zhang et al. | State-of-charge estimation of lithium-ion battery pack based on improved RBF neural networks | |
| CN114447378B (en) | Parameter optimization method for proton exchange membrane fuel cell | |
| Anbarasu et al. | Novel enhancement of energy management in fuel cell hybrid electric vehicle by an advanced dynamic model predictive control | |
| Desantes et al. | A modeling framework for predicting the effect of the operating conditions and component sizing on fuel cell degradation and performance for automotive applications | |
| CN113140765A (en) | Fuel cell air inlet flow and pressure decoupling control method and system | |
| Rašić et al. | Multi-domain and Multi-scale model of a fuel cell electric vehicle to predict the effect of the operating conditions and component sizing on fuel cell degradation | |
| Safwat et al. | Adaptive fuzzy logic control of boost converter fed by stand-alone PEM fuel cell stack | |
| Haidoury et al. | Dynamic fuel cell model improvement based on macroscopic energy representation | |
| Laird et al. | Graph-based design and control optimization of a hybrid electrical energy storage system | |
| Banaei et al. | Optimal control strategies of fuel cell/battery based zero-emission ships: A survey | |
| CN113492727B (en) | Fuel cell hybrid power system control method based on EMPC | |
| Haitao et al. | LQR-based power train control method design for fuel cell hybrid vehicle | |
| Hou et al. | Robustly Integrated Design of Plug-in Fuel Cell Electric Buses Considering the Noise Disturbance | |
| Quan et al. | A hierarchical predictive strategy-based hydrogen stoichiometry control for automotive fuel cell power system | |
| Liang et al. | Simulation and test of a fuel cell hybrid golf cart | |
| Geng et al. | A Comparative Study of Fuel Cell Prediction Models Based on Relevance Vector Machines with Different Kernel Functions | |
| Piras et al. | Hydrogen consumption and durability assessment of fuel cell vehicles in realistic driving | |
| CN113420486B (en) | Battery anode material integrated design method and system based on multi-scale simulation |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210416 |
|
| RJ01 | Rejection of invention patent application after publication |