Aviation SOFC-GT hybrid power system power matching evaluation and optimization method

Abstract

The invention discloses a power matching evaluation and optimization method for an aviation SOFC-GT hybrid power system, and the method comprises the steps: firstly, determining the total power demand of the aviation SOFC-GT hybrid power system under a typical working condition, and selecting different SOFC and GT power output combinations based on the demand; then, a plurality of key performance evaluation indexes are determined, and visual evaluation is carried out through a radar map; then, establishing a power matching performance prediction model, and performing training based on a BP neural network so as to predict the performance of different power matching schemes; on the basis, deleting redundant targets and then utilizing multi-target optimization to obtain a Pareto optimal leading edge; and finally, through a TOPSIS decision-making method, selecting a power matching scheme closest to an ideal solution. The invention provides an efficient and reliable method for the power matching of the aviation SOFC-GT hybrid power system, and is suitable for the design of the hybrid power system in the aviation field.

Description

Technical Field

[0001] The present invention belongs to the technical field of aviation fuel cell hybrid power, and in particular relates to a power matching evaluation and optimization method for an aviation SOFC-GT hybrid power system. Background Art

[0002] At present, the electrical energy on the aircraft is mainly obtained through the engine or the battery. The former has low power generation efficiency and the latter has insufficient energy density. Therefore, the development of high-efficiency and high-energy-density aviation power generation systems is an urgent problem to be solved in the aviation field.

[0003] Solid oxide fuel cells (SOFCs) have gradually become a hot topic in research and application due to their high efficiency, low emissions and fuel flexibility. SOFCs can directly convert chemical energy into electrical energy, with high energy conversion efficiency and long service life. However, SOFC power output regulation is relatively slow, which makes it perform poorly in scenarios with large fluctuations in load demand.

[0004] The turbine engine (GT) is another efficient power system with high power regulation capability and can respond quickly to load changes. It is suitable for high-dynamic scenarios that require rapid power regulation. However, when a turbine engine is used alone to drive a generator to power an aircraft, fuel consumption is high and more pollutants are emitted to the environment.

[0005] In order to make up for the shortcomings of SOFC and GT, more and more researchers have begun to explore the combination of the two to form a SOFC-GT hybrid system. The high-temperature exhaust gas of SOFC contains some unreacted fuel, which has high energy. Passing it into the combustion chamber for mixed combustion with the fuel can further improve the efficiency of the system. SOFC provides stable and efficient power output, while GT is responsible for responding to load demand fluctuations, especially providing rapid response when the system load suddenly increases. Through reasonable power matching, SOFC and GT can each play their own maximum advantages, thereby improving the performance of the overall system, reducing fuel consumption, and reducing pollutant emissions.

[0006] However, the differences in the working characteristics of SOFC and GT make how to optimize the power matching between the two a key issue. In some cases, the existing technology has the problem of single-objective optimization, often ignoring the balance of the overall system performance. For working conditions that need to provide the same power, it is still a multi-objective optimization problem to reasonably match the power output of the two to ensure the best power distribution while meeting the goals of minimum system total weight, maximum system thermal efficiency and minimum fuel consumption rate.

[0007] Therefore, there is an urgent need for an aviation SOFC-GT hybrid power system power matching evaluation and optimization method to achieve coordinated work between the two and improve the efficiency and performance of the hybrid power system. Summary of the invention

[0008] The present invention proposes a power matching evaluation and optimization method for aviation SOFC-GT hybrid power system. By evaluating the matching relationship between the power outputs of SOFC and GT, and combining the multi-objective optimization algorithm and TOPSIS decision-making method, it ensures that the total power demand is met while maximizing the overall efficiency of the system, reducing the impact on the environment and reducing fuel consumption.

[0009] The purpose of the present invention is achieved through the following technical solutions:

[0010] A power matching evaluation and optimization method for an aviation SOFC-GT hybrid power system comprises the following steps:

[0011] Step 1: In the early stage of system design, the total power demand of the aviation SOFC-GT hybrid system under typical operating conditions is clarified, and the power output combination of SOFC and GT is determined based on this demand. Matching different power output schemes can ensure that the system meets the overall power demand;

[0012] Step 2, determine the key performance evaluation indicators, and evaluate the performance of different SOFC and GT power output combinations in step 1. The performance evaluation is presented in the form of a radar chart to show the performance of different matching schemes in various indicators, and to more intuitively compare the advantages and disadvantages of different power matching schemes;

[0013] Step 3, establish a power matching performance prediction model, use the matching scheme obtained in step 2 as the input data set, and the corresponding performance evaluation index as the output data set, and randomly divide it into a training set, a test set, and a validation set, which are used for fitting, optimization, and verification of parameters in the power matching performance prediction model, respectively.

[0014] Step 4: Based on the BP (Back Propagation) neural network model, the training set obtained in step 3 is used to train the neural network model, and the validation set and test set are used to validate and test the neural network model to improve the generalization ability of the model. The nonlinear mapping relationship between input features and corresponding outputs is obtained by training and testing the data with the neural network model, and the performance prediction model of different SOFC and GT power matching schemes is obtained. The abstract function model established is:

[0015] S p (m total , η heat , η ele , m fuel, η CO2 )=S net (E SOFC , E GT ) (1)

[0016] Among them, S p is the sum of SOFC output power and GT output power; S net A performance prediction model for different SOFC and GT power matching schemes.

[0017] Step 5: Use target set clustering to delete redundant targets, so as to determine the minimum number of targets required to construct the Pareto optimal frontier. In order to meet the required power while minimizing the fuel consumption rate, maximizing the system thermal efficiency, and minimizing the fuel consumption rate, a multi-objective optimization model of performance evaluation indicators under the SOFC and GT power matching scheme is established, and the constraints on the change of SOFC and GT output power are determined:

[0018]

[0019] Step 6: Based on the multi-objective optimization model established in step 5, the MATLAB multi-objective genetic algorithm is used to solve the optimization problem of the multi-objective function. The approximate set of optimal solutions is searched through selection, crossover and mutation to obtain the Pareto solution set.

[0020] Step 7: Use the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) decision method to compare the distances between different power matching solutions and the ideal solution, and select the solution that is closest to the ideal solution. The TOPSIS method comprehensively evaluates the pros and cons of each solution by calculating the distance between each solution and the ideal solution, as well as the distance from the negative ideal solution. This method can make the best decision in multi-objective optimization and ensure that the selected power matching solution performs best in all indicators.

[0021] Furthermore, in step 1, under typical conditions, the total power demand is E total , the power provided by SOFC with different power matching schemes is E SOFC_1 、E SOFC_2 、E SOFC_3 ,……,E SOFC_n , the corresponding power provided by GT is E GT_1 、E GT_2 、E GT_3 ,……,E GT_n , each matching scheme satisfies E SOFC_i +E GT_i =E total .

[0022] In step 2, the performance evaluation indicators of different SOFC and GT power output combinations include: total system weight m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 .

[0023] Total system weight m total Mainly consider the weight of SOFC stack m SOFC and GT weight m GT , the specific calculation formula is as follows:

[0024]

[0025] m GT =0.1×E GT (5)

[0026] System thermal efficiency η heat It is an important indicator of thermal management. In the SOFC-GT hybrid system, waste heat recovery is an important way to improve thermal efficiency. The specific calculation formula is as follows:

[0027]

[0028] Among them, m fuel Fuel consumption, q fuel Indicates the calorific value of fuel, J / kg, Q sys,out Represents the heat removed from the system, W.

[0029] System power generation efficiency η ele Expressed as the efficiency of conversion from fuel to electricity, the higher the power generation efficiency, the more the system can convert the input chemical energy into electrical energy, reducing energy waste. The specific calculation formula is as follows:

[0030]

[0031] Fuel consumption rate m fuel It is mainly divided into two parts. One part is used for reforming to produce hydrogen and supply fuel to SOFC, and the other part is used to directly enter the combustion chamber for combustion. By changing the flow rate of the two fuel streams, the output power of SOFC and GT is adjusted.

[0032] CO2 emission rate η CO2 Indicates the carbon dioxide emissions generated per unit power output. The specific calculation formula is as follows:

[0033]

[0034] Among them, m CO2 is the total mass of carbon dioxide emitted by the system, calculated based on the combustion process and combustion efficiency of the fuel, kg.

[0035] In order to more intuitively display the total weight of the system m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 The impact of different power matching schemes is shown using a radar chart. The radar chart can clearly show the performance of each scheme in various indicators. Through the radar chart, we can see that some schemes have outstanding performance in certain key performances, and some schemes have balanced performance in multiple indicators, which provides an important basis for selecting the best scheme.

[0036] In step 5, when the dimension of the Pareto frontier is more than 3, it cannot be represented in space. Therefore, it is necessary to remove redundant targets by clustering the target set. This method uses the correlation coefficient to represent the relationship between targets, classifies the targets that can be represented by the correlation coefficient into one category, selects representative targets to represent, and separates the conflicting targets. This results in three performance evaluation indicators that need to be optimized: the total weight of the system m total 、System thermal efficiency η heat , fuel consumption rate m fuel .

[0037] The present invention can accurately match the power output of SOFC and GT according to different SOFC and GT power matching schemes by introducing a multi-objective optimization algorithm, a BP neural network performance prediction and a TOPSIS decision-making method, and realize a comprehensive evaluation of different power matching schemes, thereby balancing various performance indicators on the basis of ensuring the total power demand.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The present invention realizes precise matching of two different power output systems, SOFC and GT, based on radar charts, multi-objective optimization algorithms and TOPSIS decision-making methods, so that the aviation power system can significantly improve the overall energy conversion efficiency and performance of the system while meeting the total power requirements under various flight conditions. This provides a method for solving the problem that the SOFC and GT power matching schemes in traditional technologies are difficult to take into account multiple optimization objectives at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 Schematic diagram of a power matching evaluation and optimization method for an aviation SOFC-GT hybrid power system.

[0041] Figure 2Architecture diagram of aviation SOFC-GT hybrid power system.

[0042] Figure 3 The optimal solution of TOPSIS selection on the Pareto front graph. DETAILED DESCRIPTION

[0043] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0044] Example

[0045] Taking a certain aircraft SOFC-GT hybrid power system as an example, the power matching evaluation and optimization method of the aircraft SOFC-GT hybrid power system is described. The architecture diagram of the aircraft SOFC-GT hybrid power system is shown in the figure below. Figure 2 As shown in the figure, there are two main power generation units: SOFC and GT. SOFC uses the hydrogen-rich mixed gas produced by fuel reforming as fuel for chemical reaction to achieve high-efficiency, low-emission and stable power output; while GT uses the conversion of mechanical energy to electrical energy to play the role of supplementing power demand in the system. The power output of the two functional units is aggregated and adjusted by the power integration and management unit to ensure that the total power demand of the aircraft is met under this working condition.

[0046] Step 1: In the early stage of system design, the total power requirement E of the aircraft in the cruise phase is determined. total =500kW. To meet this requirement, the SOFC output power is set to a series of candidate values, such as E SOFC_1 =100kW, E SOFC_2 =110kW, E SOFC_3 =120kW, ..., E SOFC_n =400kW, and accordingly, the corresponding GT output power is E GT_1 =400kW, E GT_2 =390kW, E GT_3 =380kW, ..., E GT_n =100kW, satisfying the constraint E of each matching solution SOFC_i +E GT_i =E total , i=1,2,…,n;

[0047] Step 2: For the different power output combinations obtained in step 1, this embodiment determines the following key performance evaluation indicators.

[0048] Total system weight m total Mainly consider the weight of SOFC stack m SOFC and GT weight m GT , the specific calculation formula is as follows:

[0049]

[0050] m GT =0.1×E GT (2)

[0051] System thermal efficiency η heat It is an important indicator of thermal management. In the SOFC-GT hybrid system, waste heat recovery is an important way to improve thermal efficiency. The specific calculation formula is as follows:

[0052]

[0053] Among them, m fuel Fuel consumption, q fuel Indicates the calorific value of fuel, J / kg, Q sys,out Represents the heat removed from the system, W.

[0054] System power generation efficiency η ele Expressed as the efficiency of conversion from fuel to electricity, the higher the power generation efficiency, the more the system can convert the input chemical energy into electrical energy, reducing energy waste. The specific calculation formula is as follows:

[0055]

[0056] Fuel consumption rate m fuel It is mainly divided into two parts. One part is used for reforming to produce hydrogen and supply fuel to SOFC, and the other part is used to directly enter the combustion chamber for combustion. By changing the flow rate of the two fuel streams, the output power of SOFC and GT is adjusted.

[0057] CO2 emission rate η CO2 Indicates the carbon dioxide emissions generated per unit power output. The specific calculation formula is as follows:

[0058]

[0059] Among them, m CO2 is the total mass of carbon dioxide emitted by the system, calculated based on the combustion process and combustion efficiency of the fuel, kg.

[0060] This embodiment collects the specific values ​​of each matching solution under the above indicators and uses a radar chart to display the total system weight m of each solution. total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 The performance of different aspects can be compared intuitively, and the advantages and disadvantages of different solutions can be identified.

[0061] Step 3, in order to achieve accurate prediction of the performance of each matching scheme, the data of each scheme obtained in step 2 is used as a sample data set to construct an input-output pair. The input data set is the combination of SOFC and GT output power in each matching scheme, and the output data set is the corresponding performance index value. Subsequently, the data is randomly divided into a training set, a test set, and a validation set. The data is divided according to the ratio of 70% training, 15% testing, and 15% validation. This process provides a basis for subsequent neural network model parameter fitting, optimization, and generalization ability verification.

[0062] Step 4: Based on step 3, a performance prediction model is constructed using a BP (Back Propagation) neural network. By training the input and corresponding output of the training set data, the network can capture the nonlinear mapping relationship between the output power of SOFC and GT and various performance indicators of the system. Its abstract function model can be expressed as:

[0063] S p (m total , η heat , η ele , m fuel , η CO2 )=S net (E SOFC , E GT ) (6)

[0064] Among them, S p is the sum of SOFC output power and GT output power; S net The performance prediction model for different SOFC and GT power matching schemes; the total system weight m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 , GT output power is E GT , SOFC output power is E SOFC .

[0065] The trained neural network is verified and tested using the validation set and test set to ensure that the model has good generalization ability and prediction accuracy. 2 All of them are above 0.98, providing accurate performance prediction data for subsequent multi-objective optimization.

[0066] Step 5: After obtaining an accurate prediction model, in order to optimize the overall performance of the system, multiple performance indicators need to be optimized on the basis of meeting the total power demand. In order to avoid redundant targets affecting the optimization effect, the embodiment uses the target set clustering method (using the correlation coefficient to analyze the correlation between each indicator) to delete redundant targets and select representative targets. Finally, the three core performance indicators to be optimized are determined: total system weight m total 、System thermal efficiency η heat , fuel consumption rate m fuel At the same time, according to the actual constraints of SOFC and GT output power changes, the following optimization objectives and constraints are established:

[0067]

[0068] Step 6: Based on the multi-objective optimization model established in step 5, the MATLAB multi-objective genetic algorithm is used to solve the optimization problem of the multi-objective function. After multiple iterations, a set of optimization methods that take into account the total weight m of the system is obtained. total 、System thermal efficiency η heat , fuel consumption rate m fuel The solution set provides several solutions for the optimal power matching of aviation SOFC-GT hybrid power system under different flight conditions.

[0069] Step 7: Based on the Pareto solution set, the embodiment uses the TOPSIS decision method to comprehensively rank the candidate solutions. The TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) decision method calculates the distance between each solution and the ideal solution (the best hypothetical solution in each indicator) and the negative ideal solution (the worst hypothetical solution in each indicator) to obtain the relative proximity C. i :

[0070]

[0071] in, and are the Euclidean distances between solution i and the ideal solution and the negative ideal solution respectively. The solution with the highest relative proximity is the optimal solution.

[0072] Through the TOPSIS method, the final selected solution has a total system weight m total 、System thermal efficiency η heat , fuel consumption rate m fuelIt performs best in key indicators such as , and can maximize the overall system efficiency and reduce environmental impact and fuel consumption while ensuring the total power demand. The optimal solution selected by TOPSIS on the Pareto front diagram is as follows Figure 3 shown.

[0073] The present invention provides a method for evaluating and optimizing power matching of an aviation SOFC-GT hybrid power system. There are many methods and approaches to implement the technical solution. The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the protection scope of the present invention. All components not specified in this embodiment can be implemented by existing technologies.

Claims

1. A power matching evaluation and optimization method for an aviation SOFC-GT hybrid power system, characterized in that: The following steps are involved: Step 1: clarify the total power demand of the aviation SOFC-GT hybrid system under typical working conditions, determine the power output combination of SOFC and GT according to the demand, and match different power output schemes to ensure that the system meets the overall power demand; Step 2: Determine the key performance evaluation indicators and evaluate the performance of different SOFC and GT power output combinations in step 1. The performance evaluation is presented in the form of a radar chart to show the performance of different matching schemes in various indicators and to intuitively compare the advantages and disadvantages of different power matching schemes. Step 3, establish a power matching performance prediction model, use the matching scheme obtained in step 2 as the input data set, and the corresponding performance evaluation index as the output data set, and randomly divide it into a training set, a test set, and a validation set, which are used for fitting, optimization, and verification of parameters in the power matching performance prediction model respectively; Step 4: Based on the BP neural network model, the training set obtained in step 3 is used to train the neural network model, and the validation set and test set are used to validate and test the neural network model to improve the generalization ability of the model. The nonlinear mapping relationship between the input features and the corresponding outputs is obtained by training and testing the data through the neural network model, and the performance prediction model of different SOFC and GT power matching schemes is obtained. The abstract function model established is: Among them, S p is the sum of SOFC output power and GT output power; S net The performance prediction model for different SOFC and GT power matching schemes; the total system weight m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 , GT output power is E GT , SOFC output power is E SOFC ; Step 5: Use target set clustering to delete redundant targets, so as to determine the minimum number of targets required to construct the Pareto optimal frontier. In order to meet the required power while minimizing the fuel consumption rate, maximizing the system thermal efficiency, and minimizing the fuel consumption rate, a multi-objective optimization model of performance evaluation indicators under the SOFC and GT power matching scheme is established, and the constraints on the change of SOFC and GT output power are determined: Step 6, based on the multi-objective optimization model established in step 5, a MATLAB multi-objective genetic algorithm is used to solve the optimization problem of the multi-objective function, and an approximate set of optimal solutions is searched through selection, crossover and mutation to obtain a Pareto solution set; Step 7: Use the TOPSIS decision method to compare the distances between different power matching schemes and the ideal solution, and select the scheme closest to the ideal solution. The TOPSIS method calculates the distance between each scheme and the ideal solution, as well as the distance from the negative ideal solution, and comprehensively evaluates the pros and cons of each scheme. It makes the best decision in multi-objective optimization and ensures that the selected power matching scheme performs best in all indicators.

2. The method for power matching evaluation and optimization of an aviation SOFC-GT hybrid power system according to claim 1, characterized in that: In step 1, under typical conditions, the total power demand is E total , the power provided by SOFC with different power matching schemes is E SOFC_1 、E SOFC_2 、E SOFC_3 ,……,E SOFC_n , the corresponding power provided by GT is E GT_1 、E GT_2 、E GT_3 ,……,E GT_n , each matching scheme satisfies E SOFC_i +E GT_i =E total .

3. The method for power matching evaluation and optimization of an aviation SOFC-GT hybrid power system according to claim 1, characterized in that: In step 2, the performance evaluation indicators of different SOFC and GT power output combinations include: total system weight m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 . Total system weight m total Considering the SOFC stack weight m SOFC and GT weight m GT , the specific calculation formula is as follows: m GT =0.1×E GT System thermal efficiency η heat It is an important indicator of thermal management. In the SOFC-GT hybrid system, waste heat recovery is an important way to improve thermal efficiency. The specific calculation formula is as follows: Among them, m fuel Fuel consumption, q fuel Indicates the calorific value of fuel, J / kg, Q sys,out Indicates the heat removed from the system, W; System power generation efficiency η ele Expressed as the efficiency of conversion from fuel to electrical energy, the higher the power generation efficiency means that the system can maximize the conversion of input chemical energy into electrical energy and reduce energy waste. The specific calculation formula is as follows: Fuel consumption rate m fuel It is mainly divided into two parts. One part is used for reforming hydrogen to supply fuel to SOFC, and the other part is used to directly enter the combustion chamber for combustion. By changing the flow rate of the two fuels, the output power of SOFC and GT can be adjusted. CO2 emission rate η CO2 Indicates the carbon dioxide emissions generated per unit power output. The specific calculation formula is as follows: Among them, m CO2 It is the total mass of carbon dioxide emitted by the system, calculated based on the combustion process and combustion efficiency of the fuel.

4. The method for power matching evaluation and optimization of an aviation SOFC-GT hybrid power system according to claim 3, characterized in that: Use radar chart to visually display the total system weight m total , system thermal efficiency η heat , system power generation efficiency η ele , fuel consumption rate m fuel , CO2 emission rate η CO2 The radar chart clearly shows the impact of different power matching schemes on various indicators. Through the radar chart, we can find that some schemes have outstanding performance in certain key performances, and some schemes have balanced performance in multiple indicators, which provides an important basis for selecting the best scheme.

5. The method for power matching evaluation and optimization of an aviation SOFC-GT hybrid power system according to claim 1, characterized in that: In step 5, when the dimension of the Pareto frontier is more than 3, it cannot be represented in space. Redundant targets are deleted by clustering the target set. This method uses the correlation coefficient to represent the relationship between targets, classifies the targets that can be represented by the correlation coefficient into one category, selects representative targets to represent, and separates the conflicting targets from each other. This results in three performance evaluation indicators that need to be optimized: the total weight of the system m total 、System thermal efficiency η heat , fuel consumption rate m fuel .

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