Air compressor matching and optimizing method for variable altitude fuel cell

Abstract

According to the air compressor matching and optimizing method for the variable altitude fuel cell, theoretical and test results are used for conducting double correction on the air compressor MAP, and the accuracy of an air compressor model obtained on the basis is higher; and through multi-target particle swarm optimization in a high-dimensional parameter space, a more practical optimal solution set about the power level and the air supply condition of the air compressor can be obtained. According to the invention, air compressor matching and air supply condition adjustment along with altitude change are realized, the method is especially suitable for a two-stage centrifugal air compressor, and the reliability of a fuel cell system is also effectively ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of fuel cell system optimization control, and in particular relates to a method for matching and optimizing an air compressor for a variable altitude fuel cell. Background Art

[0002] At present, among all types of air compressors suitable for fuel cell systems, centrifugal air compressors have comprehensive advantages over other types in terms of volume, noise, flow and pressure ratio range, efficiency, response speed, etc. However, with the increasing demand for fuel cell heavy trucks and high-altitude applications, air compressors are in urgent need of significantly increasing air flow and compression ratio, and the power consumption must be increased by more than 5% from the current 15% to 20%. For this reason, the optimization and selection matching of various components of air compressors have received more and more attention in this field, and there is still a lot of room for improvement even for centrifugal air compressors. In the prior art, the matching and optimization of fuel cell air compressors are mainly considered for the optimization or placement of structural dimensions, such as the optimization of the dimensions of impellers, bearings, and housings. In addition, the analysis of heavy-duty transport and high altitude is also a single-sided study, lacking a more comprehensive solution. Therefore, how to achieve high-altitude working conditions and matching and optimization of fuel cell air compressors suitable for heavy-duty commercial vehicles to achieve the highest system efficiency is a technical problem that urgently needs to be solved in this field. Summary of the invention

[0003] In view of this, in view of the technical problems existing in the art, the present invention provides a method for matching and optimizing an air compressor for a variable altitude fuel cell, which specifically includes the following steps:

[0004] Step S1, respectively establishing a fuel cell stack model, an air supply subsystem model including an air supply pipeline model, a cathode flow model, a return pipeline model and a back pressure valve model, and an atmospheric pressure environment pressure model;

[0005] Step S2, considering the electrochemical reaction requirements of the fuel cell stack under variable altitude and the pipeline gas flow resistance characteristic model, establishing a supply condition model related to the output flow rate and compression ratio of the air compressor;

[0006] Step S3: Select several suitable air compressors according to the gas supply demand of the fuel cell stack, perform bench tests on some of the selected air compressors at different altitudes to obtain the corresponding air compressor MAP; for some air compressors that have not been tested, correct the air compressor performance by considering the atmospheric environment changes at different altitudes to obtain the air compressor MAP at different altitudes, and establish the corresponding air compressor model;

[0007] Step S4, selecting two working load points of high current and low current as input, and respectively designing three cost functions of fuel cell cathode system mass, air compressor isentropic efficiency and fuel cell system net power;

[0008] Step S5, for the matched air compressor, the above three cost functions are taken as the goal to achieve the comprehensive minimum, the air compressor speed and the back pressure valve opening are used as the control variables of the algorithm, and the multi-objective particle swarm optimization algorithm (MOPSO) is executed based on the established models to solve the multi-objective Pareto frontier under the variable altitude environment, and finally the air compressor model and its corresponding optimal solution of peroxygen ratio and compression ratio are obtained.

[0009] Furthermore, the electrochemical reaction requirement model of the battery stack in step S2 specifically adopts the following form:

[0010]

[0011] Among them, W cp,out is the output flow requirement of the air compressor, λ O2 is the peroxygen ratio, I st,max is the maximum load current, N cell is the number of battery cells, M air is the molar mass of air, F is the Faraday constant;

[0012] The flow resistance characteristics of the gas supply pipeline are specifically in the following form:

[0013] Δp=a1W cp +a2W cp +a3

[0014] Among them, W cp is the output flow of the air compressor, a1, a2, a3 are experimental experience coefficients.

[0015] Furthermore, in step S3, the performance of the air compressor is corrected by taking into account the atmospheric environment changes at different altitudes, including:

[0016] First, based on the traditional correction method, the corrected air compressor output flow is obtained:

[0017]

[0018] Among them, W r is the corrected air compressor output flow, T h , P h They are the intake air temperature and pressure corresponding to different altitudes;

[0019] The MAP of the air compressor at different altitudes is obtained again through the following corrections:

[0020]

[0021] Where ξ is the altitude correction factor.

[0022] Furthermore, in step S4, the fuel cell cathode system mass cost function is specifically adopted in the following form:

[0023]

[0024] Among them, M cp and M ca are respectively the mass of the air compressor and the mass of the fuel cell cathode system; P st,L and P st,E are the stack power at low load and rated point, i.e., high current load point; ρ fc is the specific power of the fuel cell system;

[0025] The specific form of the air compressor isentropic efficiency cost function is as follows:

[0026]

[0027] Among them, η cp,M is the highest isentropic efficiency of the air compressor; η cp,L and η cp,E are the isentropic power of the air compressor at low load and rated point respectively; W cp,L and W cp,E The output flow of the air compressor at low load and rated point respectively; PR cp,L and PR cp,E are the compression ratios of the air compressor at low load and rated points respectively; ω cp,L and ω cp,E They are the air compressor speeds at low load and rated points respectively;

[0028] The specific form of the net power cost function of the fuel cell system is as follows:

[0029]

[0030] Among them, P net,M is the maximum net power of the fuel cell system; P cp,L and P cp,E The power consumption of the air compressor at low load and rated point respectively.

[0031] Furthermore, the objective function of the multi-objective particle swarm optimization algorithm executed in step S5, as well as the constraints of the air compressor speed, oxygen ratio, compression ratio, and back pressure valve opening are specifically in the following form:

[0032]

[0033] Wherein, subscripts H and L are upper and lower limits respectively; N is the compressor serial number; θ is the back pressure valve opening.

[0034] The above-mentioned variable altitude fuel cell air compressor matching and optimization method provided by the present invention uses theoretical and experimental results to double-correct the air compressor MAP, so that the air compressor model obtained on this basis is more accurate; through multi-objective particle swarm optimization in high-dimensional parameter space, a more practical optimal solution set for air compressor power level and gas supply conditions can be obtained. The present invention realizes air compressor matching and gas supply condition adjustment with altitude changes, is particularly suitable for two-stage centrifugal air compressors, and effectively ensures the reliability of the fuel cell system. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 A flowchart of the method provided by the present invention;

[0036] Figure 2 The output performance diagram of the air compressor at different altitudes obtained based on the example of the present invention;

[0037] Figure 3 The Pareto front result under sea level obtained in the example of the present invention;

[0038] Figure 4 It is the Pareto projection of optimization results at different altitudes in the example based on the present invention. DETAILED DESCRIPTION

[0039] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] The present invention provides a method for matching and optimizing an air compressor for a variable altitude fuel cell. Figure 1 As shown, the specific steps include:

[0041] Step S1, respectively establishing a fuel cell stack model, an air supply subsystem model including an air supply pipeline model, a cathode flow model, a return pipeline model and a back pressure valve model, and an atmospheric pressure environment pressure model;

[0042] Step S2, considering the electrochemical reaction requirements of the fuel cell stack under variable altitude and the pipeline gas flow resistance characteristic model, establishing a supply condition model related to the output flow rate and compression ratio of the air compressor;

[0043] Step S3: Select several suitable air compressors according to the gas supply demand of the fuel cell stack, perform bench tests on some of the selected air compressors at different altitudes to obtain the corresponding air compressor MAP; for some air compressors that have not been tested, correct the air compressor performance by considering the atmospheric environment changes at different altitudes to obtain the air compressor MAP at different altitudes, and establish the corresponding air compressor model;

[0044] Step S4, selecting two working load points of high current and low current as input, and respectively designing three cost functions of fuel cell cathode system mass, air compressor isentropic efficiency and fuel cell system net power;

[0045] Step S5, for the matched air compressor, the above three cost functions are taken as the goal to achieve the comprehensive minimum, the air compressor speed and the back pressure valve opening are used as the control variables of the algorithm, and the multi-objective particle swarm optimization algorithm (MOPSO) is executed based on the established models to solve the multi-objective Pareto frontier under the variable altitude environment, and finally the air compressor model and its corresponding optimal solution of peroxygen ratio and compression ratio are obtained.

[0046] In a preferred embodiment of the present invention, in step S2, according to the chemical reaction principle of the fuel cell reaction and Faraday's law, the maximum air flow rate is calculated by the maximum load current, and the following form of the stack electrochemical reaction demand model is obtained in combination with the selected excess oxygen ratio range and the stack operating range:

[0047]

[0048] Among them, W cp,out is the output flow requirement of the air compressor, λ O2 is the peroxygen ratio, I st,max is the maximum load current, N cell is the number of battery cells, M air is the molar mass of air, F is the Faraday constant;

[0049] Since there are fewer turning points and less pressure loss in the air supply pipeline of the fuel cell system, the following form of air supply pipeline flow resistance characteristics can be fitted based on previous experimental data:

[0050] Δp=a1W cp +a2W cp +a3

[0051] Among them, W cp is the output flow of the air compressor, a1, a2, a3 are experimental experience coefficients.

[0052] In a preferred embodiment of the present invention, the step S3 of correcting the performance of the air compressor by taking into account the atmospheric environment changes at different altitudes includes:

[0053] First, based on the traditional correction method, the corrected air compressor output flow is obtained:

[0054]

[0055] Among them, W r are the corrected air compressor output flow, T h , P h They are the intake air temperature and pressure corresponding to different altitudes;

[0056] The MAP of the air compressor at different altitudes is obtained again through the following corrections:

[0057]

[0058] Where ξ is the altitude correction factor.

[0059] In a preferred embodiment of the present invention, in step S4, it is considered that the air compressor with large flow and high compression ratio has a correspondingly increased mass. Therefore, a trade-off needs to be made between the output capacity and mass of the air compressor, so the following fuel cell cathode system mass cost function is adopted:

[0060]

[0061] Among them, M cp and M ca are respectively the mass of the air compressor and the mass of the fuel cell cathode system; P st,L and P st,E are the stack power at low load and rated point, i.e., high current load point; ρ fc is the specific power of the fuel cell system;

[0062] In order to ensure that the air compressor works stably in the high-efficiency area, prolong the service life of the air compressor, and reduce the requirements for the air compressor cooling system, the isentropic efficiency of the air compressor is taken as an optimization target. The specific form of its cost function is as follows:

[0063]

[0064] Among them, η cp,M is the highest isentropic efficiency of the air compressor; η cp,L and η cp,E are the isentropic power of the air compressor at low load and rated point respectively; W cp,L and W cp,E The output flow of the air compressor at low load and rated point respectively; PR cp,L and PR cp,E are the compression ratios of the air compressor at low load and rated points respectively; ω cp,L and ω cp,EThey are the air compressor speeds at low load and rated points respectively;

[0065] To avoid the problem of poor performance in the low load area, the system net power at the low load point and the rated point is calculated here, and it is set as the ratio of the difference between the fuel cell output net power and the system maximum net power. The specific form is as follows:

[0066]

[0067] Among them, P net,M is the maximum net power of the fuel cell system; P cp,L and P cp,E The power consumption of the air compressor at low load and rated point respectively.

[0068] In a preferred embodiment of the present invention, the objective function of the multi-objective particle swarm optimization algorithm executed in step S5, as well as the constraints of the air compressor speed, oxygen ratio, compression ratio, and back pressure valve opening are specifically in the following form:

[0069]

[0070] Wherein, subscripts H and L are upper and lower limits respectively; N is the compressor serial number; θ is the back pressure valve opening.

[0071] In the example based on the present invention, the selected fuel cell system has a rated power of 100kW, a rated current of 455A, a number of 400 cells, and an upper pressure limit of 2.0 bar at the cathode. Five air compressors that meet the gas supply requirements of the fuel cell stack are selected, two of which are based on the variable altitude bench test MAP of the air compressor, and three are air compressor altitude change performance obtained by correction without actual testing. Figure 2 (a)-(c) show the modified compressor MAP, and its power level gradually decreases. When executing the MOPSO optimization algorithm, the population size is set to 100, the number of iterations is set to 30, the constraint ranges of the oxygen ratio and compression ratio are set to [1.5, 2.5] and [1.5, 2.5] respectively, and the working ranges of the compressor speed and back pressure valve opening are set to [0, 100000] and [0, 1] respectively. Figure 3 (a) shows the Pareto front results below sea level, Figure 3 (b)-(d) are the projections of the optimization results. Figure 4 (a) shows the optimized Cost for different altitudes. Mca Cost Pnet The Pareto projection of Figure 4 (b) Cost after optimization at different altitudes ηcp Cost Pnet It can be seen that the present invention can achieve better optimization effect for variable altitude environment.

[0072] It should be understood that the size of the serial numbers of the steps in the embodiment of the present invention does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0073] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for matching and optimizing an air compressor for a variable altitude fuel cell, specifically The following steps are involved: Step S1, respectively establishing a fuel cell stack model, an air supply subsystem model including an air supply pipeline model, a cathode flow model, a return pipeline model and a back pressure valve model, and an atmospheric pressure environment pressure model; Step S2, considering the electrochemical reaction requirements of the fuel cell stack under variable altitude and the pipeline gas flow resistance characteristic model, establishing a supply condition model related to the output flow rate and compression ratio of the air compressor; Step S3: Select several suitable air compressors according to the gas supply demand of the fuel cell stack, perform bench tests on some of the selected air compressors at different altitudes to obtain the corresponding air compressor MAP; for some air compressors that have not been tested, correct the air compressor performance by considering the atmospheric environment changes at different altitudes to obtain the air compressor MAP at different altitudes; and establish the corresponding air compressor model; Step S4, selecting two working load points of high current and low current as input, and respectively designing three cost functions of fuel cell cathode system mass, air compressor isentropic efficiency and fuel cell system net power; Step S5, for the matched air compressor, the above three cost functions are taken as the goal to achieve the comprehensive minimum, the air compressor speed and the back pressure valve opening are used as the control variables of the algorithm, and the multi-objective particle swarm optimization algorithm is executed based on the established models to solve the multi-objective Pareto frontier under the variable altitude environment, and finally the air compressor model and its corresponding optimal solution of peroxygen ratio and compression ratio are obtained.

2. The method according to claim 1, Features: The electrochemical reaction demand model of the battery stack in step S2 specifically adopts the following form: Among them, W cp,out is the output flow demand of the air compressor, λ O2 is the peroxygen ratio, I st,max is the maximum load current, N cell is the number of battery cells, M air is the molar mass of air, F is the Faraday constant; The flow resistance characteristics of the gas supply pipeline are specifically in the following form: Δp=a 1 W cp +a 2 W cp +a 3 Among them, W cp is the air compressor output flow, a 1 ,a 2 ,a 3 is the experimental experience coefficient.

3. The method according to claim 2, Features: In step S3, the performance of the air compressor is corrected by taking into account the atmospheric environment changes at different altitudes, including: First, based on the traditional correction method, the corrected air compressor output flow is obtained: Among them, W r is the corrected air compressor output flow, T h , P h They are the intake air temperature and pressure corresponding to different altitudes; The MAP of the air compressor at different altitudes is obtained again through the following corrections: Where ξ is the altitude correction factor.

4. The method according to claim 3, Features: In step S4, the fuel cell cathode system mass cost function is specifically adopted in the following form: Among them, M cp and M ca are respectively the mass of the air compressor and the mass of the fuel cell cathode system; P st,L and P st,E are the stack power at low load and rated point, i.e., high current load point; ρ fc is the specific power of the fuel cell system; The specific form of the air compressor isentropic efficiency cost function is as follows: Among them, η cp,M is the highest isentropic efficiency of the air compressor; η cp,L and η cp,E are the isentropic power of the air compressor at low load and rated point respectively; W cp,L and W cp,E The output flow of the air compressor at low load and rated point respectively; PR cp,L and PR cp,E are the compression ratios of the air compressor at low load and rated points respectively; ω cp,L and ω cp,E They are the air compressor speeds at low load and rated points respectively; The specific form of the net power cost function of the fuel cell system is as follows: Among them, P net,M is the maximum net power of the fuel cell system; P cp,L and P cp,E The power consumption of the air compressor at low load and rated point respectively.

5. The method according to claim 4, Features: The objective function of the multi-objective particle swarm optimization algorithm executed in step S5, as well as the constraints of the air compressor speed, oxygen ratio, compression ratio, and back pressure valve opening are specifically in the following form: Wherein, subscripts H and L are upper and lower limits respectively; N is the compressor serial number; θ is the back pressure valve opening.

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