Comprehensive optimization method suitable for efficiency and emission of parallel hybrid power passenger plane

Abstract

The invention provides a comprehensive optimization method suitable for the efficiency and emission of a parallel hybrid power passenger plane. Aiming at the cruising stages under different voyage conditions, the performance comparison of the hybrid power passenger plane and the reference passenger plane under different operation mixing degrees is given, and the performance advantages of the hybrid power passenger plane in the aspects of energy consumption, oil consumption and NOx and CO emission are obtained; the change rule of indexes such as battery quality, energy consumption, oil consumption, NOx and CO emission along with the operation mixing degree is given; a comprehensive performance index is given, various performances of the hybrid power passenger plane are evaluated in a unified mode, the optimal value of the running mixing degree of the hybrid power passenger plane in the cruising stage under different voyage conditions is obtained, and comprehensive optimization of the efficiency and emission of the parallel hybrid power passenger plane can be achieved.

Description

Technical Field

[0001] This invention relates to the field of hybrid-electric aircraft and their energy management technology, and in particular to a comprehensive optimization method for the performance and emissions of parallel hybrid-electric passenger aircraft. Background Technology

[0002] Current civil airliners primarily use turbofan engines for propulsion, resulting in high fuel consumption and emissions. The introduction of hybrid power technology can improve overall energy efficiency and emissions performance by sharing the engine load with electric components. However, excessively high hybridization leads to a significant increase in battery mass, reducing overall aircraft performance; conversely, insufficient hybridization hinders the realization of energy-saving and emission-reduction advantages. Therefore, a system that balances battery mass, energy consumption, fuel consumption, and NOx emissions is needed. x A comprehensive optimization method for CO2 emissions is needed. However, current research on hybrid-electric passenger aircraft focuses mainly on structural design and power matching, lacking a comprehensive optimization method for performance and emissions under different flight range conditions.

[0003] This invention compares the performance of a hybrid-powered passenger aircraft with a baseline aircraft under different operating hybridization levels during the cruise phase under varying flight range conditions. The results show that the hybrid-powered aircraft performs better in terms of energy consumption, fuel consumption, and NOx emissions. x Performance advantages in terms of CO2 emissions; summarized battery quality, energy consumption, fuel consumption and NO2 emissions. x This paper studies the variation patterns of indicators such as CO2 emissions with operating mixing ratio and proposes a comprehensive performance index to uniformly evaluate the battery quality, energy consumption, fuel consumption, and NO2 emissions of hybrid passenger aircraft. x This invention analyzes five performance metrics, including CO2 emissions, to obtain the optimal hybrid power ratio for the cruise phase of a parallel hybrid passenger aircraft under different flight range conditions, achieving comprehensive optimization of the aircraft's performance and emissions. The proposed method for comprehensive optimization of the performance and emissions of a parallel hybrid passenger aircraft can achieve improvements in battery quality, energy consumption, fuel consumption, and NOx emissions. x The overall optimal combination of five indicators, including CO emissions. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a comprehensive optimization method for the performance and emissions of parallel hybrid passenger aircraft.

[0006] Another objective of this invention is to provide a comprehensive optimization device for the performance and emissions of parallel hybrid passenger aircraft.

[0007] The third objective of this invention is to provide a computer device.

[0008] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0009] To achieve the above objectives, a first aspect of the present invention proposes a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft, comprising: S1. Establish a normalized model that includes five performance indicators: battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. S2, calculate the dynamic weights based on the relative decrease ratio between the maximum and minimum values ​​of each performance index; S3, under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume, traverses the range of operating mixture during the cruise phase and calculates the comprehensive performance index. S4 outputs the optimal value of the running mixture that minimizes the overall performance index, thus completing the mixture optimization design.

[0010] In one embodiment of the present invention, S1 includes: S11, the five performance index normalization model specifically includes battery quality normalization index Energy consumption normalization index Fuel consumption normalization index NOx emission normalization index CO emission normalization index ; S12, the minimum and maximum values ​​of each index in the normalized model are determined by flight profile simulation data.

[0011] In one embodiment of the present invention, S2 includes: S21, Relative reduction ratio in dynamic weight calculation Through formula Confirmed, among which and These correspond to the minimum and maximum values ​​of each performance metric when traversing the range of mixing degrees, respectively; S22, Weight The normalization process uses the formula This ensures that the total weight is 1 and that the priority is dynamically adjusted according to the magnitude of changes in the indicators.

[0012] In one embodiment of the present invention, S3 includes: S31, Takeoff weight constraints include aircraft takeoff mass and landing quality ,in It is the sum of the wet mass of the two turbofan engines, the mass of passengers and crew, the mass of fuel, the mass of electrical components, and the mass of other structures. S32, the total temperature constraint condition before the engine turbine is: The low-pressure compressor venting capacity constraint is: The inlet flow rate of the low-pressure compressor.

[0013] In one embodiment of the present invention, S4 includes: S41, Run the optimal mixing value The minimum point is determined using gradient descent. When traversing the mixing degree range with a step size of 1%, the minimum point is determined using the formula. Calculate the overall performance index; S42, The optimized design results must meet the constraints. ,in This includes takeoff weight, turbine inlet total temperature, low-pressure compressor exhaust volume, and the proportion of electrical power required. .

[0014] To achieve the above objectives, a second aspect of the present invention provides a comprehensive optimization device for the efficiency and emissions of a parallel hybrid passenger aircraft, comprising: The performance index normalization module is used to establish a normalization model for five performance indicators, including battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. The dynamic weight calculation module is used to calculate dynamic weights based on the relative decrease ratio between the maximum and minimum values ​​of each performance indicator. The constraint mixture traversal module is used to traverse the operating mixture range during the cruise phase and calculate the comprehensive performance index under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume. The comprehensive performance index optimization module is used to output the optimal value of the running mixture that minimizes the comprehensive performance index, thus completing the mixture optimization design.

[0015] The present invention discloses a method and apparatus for comprehensive optimization of the performance and emissions of a parallel hybrid passenger aircraft. This method and apparatus can achieve comprehensive optimization of five performance indicators of the parallel hybrid passenger aircraft, namely battery quality, energy consumption, fuel consumption and NOx and CO emissions, under different flight conditions. It effectively balances the impact of hybridization on energy efficiency improvement and structural airworthiness, thereby improving the overall operational economy and environmental performance.

[0016] To achieve the above objectives, a third aspect of this application provides a computer device comprising a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing a comprehensive optimization method for performance and emissions of a parallel hybrid passenger aircraft as described in the first aspect embodiment.

[0017] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium storing a computer program that, when executed by a processor, implements a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft as described in the first aspect embodiment.

[0018] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0019] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 This is a flowchart of a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft according to an embodiment of the present invention; Figure 3 The turbine inlet total temperature according to an embodiment of the present invention A schematic diagram illustrating the changes in mixture during the cruise phase; Figure 4 Fuel efficiency according to embodiments of the present invention A schematic diagram illustrating the changes in mixture during the cruise phase; Figure 5 The low-pressure compressor outlet venting volume W according to an embodiment of the present invention is... bld-lpc A schematic diagram illustrating the changes in mixture during the cruise phase; Figure 6 According to an embodiment of the present invention, the ratio of the electrical power required during the cruise phase to the maximum electrical power required during the takeoff phase is... A schematic diagram illustrating the changes in mixture during the cruise phase; Figure 7 This is a schematic diagram illustrating the impact of the cruise phase operation mixing degree on the battery quality of a hybrid passenger aircraft according to an embodiment of the present invention; Figure 8 This is a schematic diagram illustrating the impact of the cruise phase operation hybrid degree on the energy consumption of a hybrid passenger aircraft during the cruise phase, according to an embodiment of the present invention. Figure 9 This is a schematic diagram illustrating the impact of the cruise phase operation hybridization degree on the energy consumption of a hybrid-powered passenger aircraft throughout the entire flight mission, according to an embodiment of the present invention. Figure 10 This is a schematic diagram illustrating the impact of the cruise phase operation mixture degree on the fuel consumption of a hybrid passenger aircraft during the cruise phase, according to an embodiment of the present invention. Figure 11This is a schematic diagram illustrating the impact of the cruise phase operation hybridization degree on the fuel consumption of a hybrid-electric passenger aircraft throughout the entire flight mission, according to an embodiment of the present invention. Figure 12 According to an embodiment of the present invention, the cruise phase operation mixture of a hybrid-electric passenger aircraft affects the NO pollutant during the cruise phase. x Schematic diagram illustrating the impact of emissions; Figure 13 According to an embodiment of the present invention, the cruise phase operation of the mixture affects the NO pollutant concentration of a hybrid-electric passenger aircraft throughout the entire flight mission. x Schematic diagram illustrating the impact of emissions; Figure 14 This is a schematic diagram illustrating the impact of the cruise phase operating mixture degree on the CO emissions of a hybrid-electric passenger aircraft during the cruise phase, according to an embodiment of the present invention. Figure 15 This is a schematic diagram illustrating the impact of the cruise phase operating mixture degree on the CO emissions of a hybrid-electric passenger aircraft throughout the entire flight mission, according to an embodiment of the present invention. Figure 16 The weights of normalized performance indicators for different flight ranges according to embodiments of the present invention. A diagram illustrating the possible values; Figure 17 These are comprehensive performance indicators according to embodiments of the present invention. A schematic diagram illustrating the changes in mixture during the cruise phase; Figure 18 This is a schematic diagram illustrating the optimization results of the cruise phase operation mixture as the flight distance changes, according to an embodiment of the present invention. Figure 19 This is a flowchart of a comprehensive optimization device for the performance and emissions of a parallel hybrid passenger aircraft according to an embodiment of the present invention; Figure 20 It is a computer device according to an embodiment of the present invention. Detailed Implementation

[0020] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0022] The following description, with reference to the accompanying drawings, describes a method and apparatus for comprehensively optimizing the efficiency and emissions of a parallel hybrid passenger aircraft according to an embodiment of the present invention.

[0023] Example 1 Figure 1 This is a flowchart of a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft according to an embodiment of the present invention, such as... Figure 1 As shown, it includes: S1. Establish a normalized model that includes five performance indicators: battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions.

[0024] Specifically, this invention constructs a normalized model comprising five performance indicators: battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. This model is used to comprehensively evaluate the operational hybrid power ratio of parallel hybrid passenger aircraft under different flight conditions. The core of this model lies in mapping each performance indicator to a unified dimensionless interval, thereby achieving quantitative comparison and weighted fusion of multi-objective optimization problems.

[0025] Furthermore, the normalization model linearly maps the performance index values ​​of the hybrid-electric aircraft under different operating mixing degrees to the [0,1] interval by comparing them with the maximum and minimum values ​​that the aircraft can achieve within the mixing degree range. Specifically, for the th Performance indicators Its normalized value Defined as:

[0026] in, and These represent the minimum and maximum values ​​of the performance index within the operating range. This formula allows performance indices with different dimensions to be standardized into comparable values, facilitating subsequent weighted aggregation.

[0027] Furthermore, the five performance indicators in this invention correspond to: converted battery mass Energy consumption Fuel consumption NOx emissions CO emissions In the normalized model, the minimum and maximum values ​​of each indicator are determined by running the mixture during the cruise phase. The results were obtained by iterating through the range (0~20%) with a step size of 1%. For example, as the degree of mixing increases, the battery quality increases significantly, while energy consumption and fuel consumption show a downward trend, NOx emissions decrease, and CO emissions first increase and then tend to stabilize.

[0028] Furthermore, this normalized model is widely applied to the operational blending optimization design of hybrid-powered passenger aircraft under different range conditions. By normalizing various performance indicators, dimensional differences can be effectively eliminated, providing a basis for subsequent weighted comprehensive performance indicators. It provides foundational data. In practice, this model, combined with a weighting method, is used to evaluate the overall performance under different mixing degrees, thus providing a quantitative basis for the formulation of energy management strategies.

[0029] Furthermore, the introduction of a normalization model significantly improves the systematicity and comparability of performance evaluation. By unifying the five performance indicators to the [0,1] range, the relative optimization potential of each indicator under varying degrees of mixing can be intuitively reflected. For example, the normalized value of battery mass can be converted into this value. It is most sensitive to changes in the degree of mixing, and its weight The dominant position of battery quality in overall performance indicators reflects its significant impact on overall performance. This model provides key technical support for achieving a multi-objective balance between efficiency and emissions in hybrid passenger aircraft.

[0030] Furthermore, S1 includes: S11, the five performance index normalization model specifically includes battery quality normalization index Energy consumption normalization index Fuel consumption normalization index NOx emission normalization index CO emission normalization index .

[0031] Specifically, this step involves normalizing five key performance indicators of the parallel hybrid-electric passenger aircraft during the cruise phase to achieve unified evaluation and optimization of multi-objective performance. The normalization model maps each performance indicator to a unified dimensionless interval [0,1], eliminating dimensional differences between different physical quantities, thereby facilitating weighted comparisons in the comprehensive performance indicators.

[0032] Furthermore, the normalization model is based on the maximum-minimum normalization method, which linearly maps the actual value of a performance indicator to its maximum and minimum values ​​within the range of operational mixing. Specifically, the battery quality normalization index... This indicates the relative position of the converted battery mass within the range of mixing degree variation, where The converted battery mass under the current operating hybrid conditions. and These represent the minimum and maximum values ​​of the indicator within the mixing range, respectively. Similarly, energy consumption, fuel consumption, NOx, and CO emissions are also normalized using the same method, with the formulas as follows: , , , The normalization process must be carried out under the premise of satisfying five constraints to ensure that the optimization results are within the feasible range of the project.

[0033] Furthermore, the normalization model relies on the extreme values ​​of five performance metrics calculated under different operating conditions. For example, the conversion of battery mass... The range of values ​​is determined by both battery capacity requirements and takeoff mass limitations, with an upper limit of [value missing]. Energy consumption is typically limited by the maximum takeoff mass m_TO and structural design margins. and fuel consumption The extreme values ​​were obtained through flight profile simulation, with units of . and The extreme values ​​of NOx and CO emissions, E_NO_x and E_co, are based on the engine emission model and the influence of mixture ratio on combustion state, and the units are respectively... and .

[0034] Furthermore, this normalized model is widely applied in the cruise phase operation mixture optimization design of hybrid-electric passenger aircraft. By normalizing various performance indicators, it can effectively support the comprehensive performance indicators in subsequent steps. The weighted calculation allows for global optimization of the operational mix under different flight conditions. For example, in short-range flights, energy consumption and fuel consumption have higher optimization weights; while in long-range flights, the weight of the normalized battery quality index is significantly increased to avoid exceeding takeoff quality limits.

[0035] Furthermore, through normalization, performance indicators that originally had different dimensions and orders of magnitude are unified to the same numerical scale, providing standardized input variables for multi-objective optimization. At the same time, the normalized model can intuitively reflect the relative improvement of each performance indicator under changes in hybridization, laying the foundation for subsequent weighted comprehensive optimization and improving the balance between efficiency and emissions of the hybrid-electric passenger aircraft.

[0036] S12, the minimum and maximum values ​​of each index in the normalized model are determined by flight profile simulation data.

[0037] Specifically, the minimum and maximum values ​​of each indicator in the normalized model are determined through flight profile simulation data, and its technical implementation is based on the performance comparison analysis of the parallel hybrid passenger aircraft under different operating mixture conditions. [Wu Zi 2] At this time, the electric propulsion system undertakes part of the propulsion power during the cruise phase, thus having a significant impact on energy consumption, fuel consumption and pollutant emissions.

[0038] Furthermore, this step first extracts various performance parameters based on flight profile simulation data at mixture ratios of 0% and 20%, including the converted battery mass. Energy consumption Fuel consumption pollutants Emissions and pollutant CO emissions By comparing and analyzing the data, the maximum and minimum values ​​of each indicator within the range of mixing degree were determined, serving as the basis for normalization calculations. The normalization formula is as follows:

[0039] in, Indicates the first The actual value of each performance indicator and These represent the minimum and maximum values ​​of the indicator within the range of mixing degree variation, respectively. Normalized performance indicator. Used for subsequent weighted composite performance metrics The construction of this system enables the optimized design of the hybrid power passenger aircraft's operating degree of hybrid power during the cruise phase under different range conditions.

[0040] Furthermore, the mixing range is set to The step size is 1% to ensure coverage of key performance inflection points. The minimum and maximum values ​​of all indicators in the normalized model are based on simulation data, possessing clear physical meaning and engineering feasibility. For example, when the hybridization degree is 0, the battery mass is zero, and energy consumption, fuel consumption, and emissions are consistent with the baseline aircraft; when the hybridization degree is 20%, the battery mass increases significantly, but energy consumption, fuel consumption, and emissions also increase. Emissions decreased significantly, while CO emissions showed a trend of first increasing and then decreasing.

[0041] Furthermore, this step is applicable to cruise phase performance optimization under different range conditions, especially in short-to-medium-range and long-range flight missions, where the impact of hybridization on energy consumption, emissions, and battery quality varies significantly. By setting a reasonable normalization benchmark, a unified evaluation of the hybrid power system under different operating strategies can be achieved, providing a quantitative basis for aircraft design and energy management.

[0042] Furthermore, through normalization, performance indicators of different dimensions and magnitudes are unified into the [0,1] interval, facilitating subsequent weighted comprehensive analysis. Its innovation lies in combining flight profile simulation data to establish a normalized model based on the degree of mixing, providing standardized input for multi-objective optimization, thereby improving the balance between efficiency and emissions of the hybrid-electric passenger aircraft.

[0043] S2, calculate the dynamic weights based on the relative decrease ratio between the maximum and minimum values ​​of each performance index.

[0044] Specifically, calculating dynamic weights based on the relative decrease in the maximum and minimum values ​​of each performance indicator is a key step in achieving comprehensive optimization of the performance and emissions of parallel hybrid-powered passenger aircraft. This step quantifies the sensitivity differences of different performance indicators within the range of operational mixing, thereby dynamically adjusting their weights in the overall performance indicators to ensure that the optimization results have higher adaptability and scientific validity.

[0045] Furthermore, this step is first based on normalized performance metrics. ,in These correspond to the conversion of battery mass, energy consumption, fuel consumption, NOx emissions, and CO emissions, respectively. The normalization operation eliminates dimensional differences by comparing the actual performance value with the minimum and maximum values ​​achieved within the operating mixture range, facilitating unified evaluation of multiple indicators. The normalization formula is:

[0046] Based on this, the relative reduction ratio of each performance index is further calculated. Its definition is:

[0047] This ratio reflects the relative improvement of a performance indicator from its maximum to its minimum value within a range of operational mixing. The more significant the performance improvement, the higher the corresponding... The larger the value, the higher the weight should be assigned in the overall performance index.

[0048] Furthermore, and The mixture is run by traversing the cruise phase in 1% increments. The obtained minimum and maximum performance values. For example, converting battery mass. It increases significantly with increasing degree of mixing. The large value indicates that it is extremely sensitive to changes in the degree of mixing; while the energy consumption and CO emissions The change was relatively small, corresponding to and The lower the value, the smaller the weight.

[0049] Furthermore, this step is applicable to operational hybridization optimization during the cruise phase under different flight range conditions. In practical engineering, the optimized design of hybrid-electric aircraft needs to consider multiple performance indicators, and the response characteristics of each indicator to hybridization vary significantly. Through dynamic weight calculation, the optimization model can be made more closely aligned with actual needs; for example, in short-range flights, more attention should be paid to fuel consumption and emissions, while in long-range flights, a balance needs to be struck between battery mass and overall energy consumption.

[0050] Furthermore, this step, by introducing a dynamic weighting mechanism, effectively solves the problem of fixed weights and difficulty in adapting to different operating conditions in traditional weighted methods. The resulting comprehensive performance index... for:

[0051] Among them, weight Depend on Normalization yields:

[0052] This method ensures that the optimization priority of performance indicators can be automatically adjusted under different flight ranges, thereby achieving the optimal balance between efficiency and emissions in the parallel hybrid passenger aircraft and improving the overall system performance and environmental friendliness.

[0053] Furthermore, S2 includes: S21, Relative reduction ratio in dynamic weight calculation Through formula Confirmed, among which and These correspond to the minimum and maximum values ​​of each performance index when traversing the range of mixing degrees.

[0054] Specifically, the relative reduction ratio in the dynamic weight calculation Through formula The technical implementation principle is based on a quantitative assessment of the relative optimization potential of each performance index within the range of operational mixing. This step, through normalization, unifies performance indices with different dimensions to the same numerical scale, thereby providing a basis for subsequent weighted comprehensive performance indices. Provide a reasonable basis for weight allocation.

[0055] Furthermore, and They represent the first The performance indicators covered the range of operating conditions during the cruise phase. The minimum and maximum values ​​obtained at that time. For example, when converting battery mass. Its maximum value This corresponds to the battery mass when the cruise mix is ​​20%, while the minimum value This corresponds to the baseline battery mass when the cruise mixture is 0%. (Using the formula...) This allows us to calculate the relative decrease in the performance index within the range of mixing degree changes, which is a quantitative indicator of its optimization potential. The larger this percentage is, the more significant the improvement in the performance index during the mixing degree change process, and therefore it should be given a higher weight in the comprehensive performance index.

[0056] Furthermore, in this step The calculations must meet five set constraints, including the aircraft takeoff mass. Landing quality Turbine front total temperature Low pressure shaft speed and high-pressure shaft speed The upper and lower limits are required. In practical applications, this step is usually implemented in a simulation platform. By traversing the cruise mixture and recording the extreme values ​​of each performance index, the values ​​are then substituted into the formula for calculation to support subsequent multi-objective optimization decisions.

[0057] Furthermore, by quantifying the optimization potential of each performance indicator, a comprehensive performance indicator can be developed. This method provides a scientific basis for weight allocation, thereby achieving a balance between battery quality, energy consumption, fuel consumption, and pollutant emissions in multi-objective optimization. Under different flight conditions, the method can dynamically adjust the weights to ensure that the optimization results have practical engineering significance and system feasibility.

[0058] S22, the weight The normalization process uses the formula This ensures that the total weight is 1 and that the priority is dynamically adjusted according to the magnitude of changes in the indicators.

[0059] Specifically, the weight The normalization process uses the formula Its technical implementation principle is based on the normalized weighting method in multi-objective optimization, which aims to transform the relative changes of different performance indicators into unified weight coefficients, thereby improving the overall performance index. The construction process enables the reasonable allocation and dynamic adjustment of various performance parameters.

[0060] Furthermore, weight The calculation depends on performance metrics maximum value and minimum value The relative reduction ratio between Its definition This ratio reflects the optimization potential of a performance indicator within the range of operational mixing; that is, the greater the range of change, the higher the sensitivity of the indicator to mixing, and therefore it should be given a higher weight in the comprehensive performance evaluation. Normalization is achieved by adjusting the various... The values ​​are normalized and summed to ensure that the total weight is 1, thereby avoiding the excessive dominance of a single indicator on the overall performance indicator and achieving balanced optimization of multiple indicators.

[0061] Furthermore, The value of is usually between [0,1], and its magnitude depends on the performance metric. The changing trend. For example, converting battery mass. It increases significantly with increasing operating mixing degree, therefore its The value is relatively large, corresponding to The weight is also relatively high; while energy consumption and CO emissions The change was relatively small, its and The values ​​are lower, and the weights are also smaller. This method conforms to industry standards for the quantitative evaluation of multiple performance indicators in aircraft design, such as the normalization principle for emission indicators in the ISO 14000 series environmental management systems.

[0062] Furthermore, this step is used to optimize the operational hybrid power ratio of the parallel hybrid-electric passenger aircraft under different flight conditions. By dynamically adjusting the weights, the system can automatically optimize the overall performance indicators based on the differences in the importance of various performance indicators in actual flight missions. This improves the overall efficiency and environmental performance of the hybrid power system. From a technical perspective, this normalized weighting method effectively addresses the issues of inconsistent dimensions and large differences in variation among multiple performance indicators, enhancing the scientific rigor and practicality of the optimization results and providing a quantitative basis for energy management and control strategies for hybrid passenger aircraft.

[0063] S3, under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume, traverses the range of operating mixture during the cruise phase and calculates the comprehensive performance index.

[0064] Specifically, under the constraints of takeoff weight, total engine turbine inlet temperature, and compressor exhaust volume, the operating mixture range during the cruise phase is traversed and comprehensive performance indicators are calculated. This step achieves quantitative evaluation and optimization decision-making of the hybrid power system operation strategy by setting a reasonable operating mixture range during the cruise phase and combining the normalization and weighted synthesis of multiple performance indicators.

[0065] Furthermore, during the cruise phase, the operating mixture... This indicates the proportion of the electric propulsion system in the total thrust. This invention first considers engine performance parameters (such as turbine inlet total temperature T_t4, fuel efficiency, etc.). Low-pressure compressor outlet gas discharge volume The proportion of electrical power required during the cruise phase to the maximum electrical power required during the takeoff phase. Based on the changing trend of the operating mixture, the reasonable range of the cruise mixture is determined as follows: Within this range, the operating hybridity is iterated in 1% increments, and five performance indicators are calculated for each hybridity: converted battery mass. Energy consumption Fuel consumption , Emissions and CO emissions Each indicator is expressed using relative indicators. , , , , Normalization is performed to eliminate dimensional differences and facilitate standardized comparison.

[0066] Furthermore, during the calculation process, it is necessary to ensure the takeoff weight of the passenger aircraft. Not exceeding the maximum allowable value Meanwhile, the total temperature before the turbine, T_t4, must not exceed the design limit. Low-pressure compressor outlet gas discharge It needs to be controlled within the allowable surge margin range. In addition, it must also meet the following requirements. The constraints, among which These correspond to the constraint boundaries of the five performance indicators. Normalized performance indicators Through formula Calculation, where These are actual performance values. and These are the minimum and maximum values ​​that the indicator achieves within the range of mixing.

[0067] Furthermore, this step is applicable to cruise phase optimization design under different range conditions, such as short-range (<1500km), medium-range (1500–3000km), and long-range (>3000km) flight missions. In practical applications, the mixture degree needs to be discretized and traversed in conjunction with flight profiles, engine performance curves, battery capacity limitations, and emission standards to ensure that the optimization results meet actual flight constraints and operational requirements.

[0068] Furthermore, this step allows us to obtain the optimal operating mixing degree under the conditions of satisfying structural and thermal constraints. This makes the overall performance index Reaching the minimum value. Among them, the weights... Normalized variation of each indicator The result was obtained through normalization. This method effectively balances the contradiction between increased battery mass and reduced energy consumption and emissions, providing a scientific basis for the energy management strategy of hybrid-electric passenger aircraft and possessing significant engineering practical value.

[0069] Furthermore, S3 includes: S31, Takeoff weight constraints include aircraft takeoff mass and landing quality ,in It is the sum of the wet mass of the two turbofan engines, the mass of passengers and crew, the mass of fuel, the mass of electrical components, and the mass of other structures.

[0070] Specifically, in the optimization design process of parallel hybrid-powered passenger aircraft, setting takeoff weight constraints is a crucial step in ensuring a balance between flight safety and system performance. This step provides boundary conditions for subsequent hybridization optimization by clarifying the maximum permissible mass of the aircraft during takeoff and landing. Specifically, takeoff mass... and landing quality It is set based on the limits on maximum takeoff weight (MTOW) and maximum landing weight (MLW) in aviation airworthiness standards (such as FAAFAR25 or EASACS-25) to ensure that the aircraft meets key indicators such as structural strength, takeoff and landing performance and fuel efficiency during takeoff and landing.

[0071] Furthermore, takeoff mass It is the sum of the masses of multiple subsystems, including the wet mass of the two turbofan engines, the mass of passengers and crew, the mass of fuel, the mass of electrical components, and the mass of other structures. The wet mass of a single turbofan engine is 1.56 times its dry mass, according to the formula:

[0072] The dry mass of a single engine can be calculated as follows: Thus, the wet mass is obtained. Passenger and crew mass is estimated based on average values ​​and baggage allowances; for example, the average passenger mass is... The crew members are And consider 50% of passengers checking in an extra piece of luggage. The luggage, with a final total weight of The fuel mass is dynamically calculated based on the fuel consumption requirements of the flight profile, while the mass of the electrical components is estimated based on the maximum electrical power requirements and power density.

[0073] Furthermore, in practical applications, this step is primarily used in the flight mission planning and system integration design phases to ensure that the introduction of the hybrid power system does not lead to excessive aircraft weight, thereby affecting takeoff and landing performance and flight safety. Simultaneously, takeoff weight limitations directly impact battery capacity configuration, as battery weight increases with hybridization; exceeding this limit... The upper limit will render the task unexecutable.

[0074] Furthermore, this step provides physical constraints for hybridization optimization, preventing flight performance from being sacrificed due to over-design of the power system. By setting a clear upper limit on mass, the optimization algorithm can be effectively guided to find the optimal solution within the feasible region, thereby achieving comprehensive optimization of efficiency and emissions.

[0075] S32, the total temperature constraint condition before the engine turbine is: The low-pressure compressor venting capacity constraint is: The inlet flow rate of the low-pressure compressor.

[0076] Specifically, in the optimization design process of a parallel hybrid passenger aircraft, the total temperature constraint condition before the engine turbine... Constraints on low-pressure compressor venting The low-pressure compressor inlet flow rate is a key technical parameter for ensuring safe engine operation and overall system efficiency. This step is based on the coupled analysis of engine thermodynamic cycle characteristics and aerodynamic performance to ensure that the thermal load and aerodynamic stability of the engine's core components remain within a controllable range during the operation of the hybrid power system.

[0077] Furthermore, the total temperature before the turbine It is a core parameter for measuring the thermal stress and material tolerance of hot-end components of an engine (such as turbine blades). When operating with a mixture of... When the electric propulsion system generates more thrust, the engine's mechanical power demand decreases, leading to an increase in combustion chamber temperature, which in turn... Rise. To prevent turbine blades from overheating and failing, a setting must be made. The upper limit is This value is typically determined based on engine design specifications and material temperature limits. During the cruise phase, this can be achieved by adjusting the engine fuel flow rate and combustion chamber fuel supply strategy. Dynamic control is used to ensure that it does not exceed the threshold.

[0078] Furthermore, the low-pressure compressor exhaust volume The constraint is closely related to the compressor surge boundary. When the operating mixture increases, the mechanical load on the low-pressure compressor decreases. To maintain stable compressor operation, the flow ratio needs to be adjusted by venting to avoid surge caused by insufficient flow. This constraint condition The inlet flow rate is the upper limit set based on the surge margin design standard of low-pressure compressors, ensuring that the compressor can still work stably under different mixing conditions and avoiding the impact on engine efficiency due to excessive exhaust volume.

[0079] Furthermore, this constraint applies to the operational mixture optimization process during the cruise phase. In actual flight missions, the cruise phase accounts for a significant portion of the total flight energy consumption. Therefore, for and The control of air mixture directly affects the engine's thermal and mechanical efficiency. By setting reasonable constraint boundaries, it is possible to effectively avoid engine performance degradation or system quality issues caused by improper air mixture settings.

[0080] Furthermore, this provides safe boundary conditions for subsequent operational mixture optimization, ensuring that while increasing mixture ratio to reduce fuel consumption and pollutant emissions, the engine's thermodynamic and aerodynamic performance limits are not exceeded. This is achieved by... and As a constraint, a balance can be achieved between performance improvement and structural safety in the hybrid power system, providing a comprehensive performance index. This lays the foundation for optimization.

[0081] S4 outputs the optimal value of the running mixture that minimizes the overall performance index, thus completing the mixture optimization design.

[0082] Specifically, this invention searches for a comprehensive performance index by performing traversal calculations within the mixed-degree range of the cruise phase, with a step size of 1%. Minimum running mixture optimal value This completes the comprehensive optimization design of the parallel hybrid-electric passenger aircraft in terms of both efficiency and emissions. This step is based on normalized performance indicators. and weighted comprehensive performance index ,in Indicates the first The normalized value of the performance index, The weights of the normalized performance index are calculated using the following formula: , It is used to reflect the relative optimization potential of various performance indicators within the range of mixing degree variation.

[0083] Furthermore, the system first determines the operational mixture variation range during the cruise phase based on the flight mission's range length, and then performs a discretization search within this range in 1% increments. For each mixture value... The system invokes the performance indicator change model to calculate the corresponding converted battery weight, energy consumption, and fuel consumption. Normalized values ​​of emissions and CO emissions to And according to weight By performing a weighted summation, the comprehensive performance index under this degree of mixing is obtained. By comparing all the mixing degrees corresponding to The value, the system's final output makes Minimum running mix This is the optimal operating hybrid configuration for a hybrid-electric passenger aircraft at this range.

[0084] Furthermore, in practical applications, this step requires combining multi-source input information such as flight profile data, engine performance curves, and battery capacity-mass relationship models to ensure balanced optimization of multi-objective performance while meeting constraints (such as takeoff mass, turbine inlet total temperature, and low-pressure compressor exhaust volume). Its technical value lies in using quantitative analysis to integrate key performance indicators such as battery mass, energy consumption, fuel consumption, and pollutant emissions into the optimization framework. This avoids the system imbalance problems caused by single-performance optimization in traditional designs, significantly improving the overall operating efficiency and environmental performance of hybrid-electric passenger aircraft across different ranges.

[0085] Furthermore, S4 includes: S41, Run the optimal mixing value The minimum point is determined using gradient descent. When traversing the mixing degree range with a step size of 1%, the minimum point is determined using the formula. Calculate the overall performance index.

[0086] Specifically, the optimal value of the running mixture The minimum point is determined using gradient descent. When traversing the mixing degree range with a step size of 1%, the minimum point is determined using the formula. Calculate the comprehensive performance index. This step achieves comprehensive optimization of the efficiency and emissions of the parallel hybrid passenger aircraft. Its technical implementation is based on the normalization and weighted fusion of multi-objective performance indexes, aiming to find the optimal operating hybrid configuration that maximizes comprehensive performance while meeting constraints.

[0087] Furthermore, gradient descent is used to find comprehensive performance indicators within a continuous range of operating mixtures. The minimum point. In specific operations, first within the range of mixing... Within this range, a discretization traversal is performed with a step size of 1%. For each mixing degree value... Through the calculation process, five normalized performance indicators were obtained. to These correspond to the conversion of battery weight, energy consumption, fuel consumption, NOx emissions, and CO emissions, respectively. The normalization process is achieved through the formula... Implementation, in which For the first The actual value of each performance indicator and These represent the minimum and maximum values ​​of the indicator within the mixing range, respectively. The normalized indicator. Assigned corresponding weights The weights are determined by the formula. The calculation shows that, among which Indicates the first The relative decrease of an indicator between its maximum and minimum values ​​is used to reflect its impact on overall performance.

[0088] Furthermore, the gradient descent step size is set to 1% to ensure sufficient resolution across the range of mixing variations to capture subtle changes in performance metrics. Normalized performance metrics. The value range is [0,1], which facilitates a unified comparison of performance parameters with different dimensions. Weight The calculation is based on the relative change of each indicator, so that indicators that have a greater impact on overall performance (such as converted battery mass) are considered. It has a higher weight, so its changing trend is given priority in the optimization process.

[0089] Furthermore, this step is applicable to the operational mixture optimization design of hybrid-electric passenger aircraft during the cruise phase under different range conditions. By performing ergonomic calculations on the mixture under constraints (such as takeoff mass, engine temperature, and speed), the optimal operational mixture can be determined for short-range, medium-range, and long-range flights respectively. This achieves comprehensive optimization of energy consumption, fuel consumption, and emissions.

[0090] Furthermore, this step introduces gradient descent and a weighted comprehensive performance index. This effectively solves the problem of conflicting indicators in multi-objective optimization problems. Through the formula... The calculation can quantify the overall performance advantages and disadvantages under different hybrid configurations, provide a scientific basis for the energy management strategy of hybrid passenger aircraft, and improve its economy and environmental protection in actual flight missions.

[0091] S42, The optimized design results must meet the constraints. ,in This includes takeoff weight, turbine inlet total temperature, low-pressure compressor exhaust volume, and the proportion of electrical power required. .

[0092] Specifically, this invention ensures a balance between engineering feasibility and performance objectives by setting five constraints during the hybridization optimization process of a parallel hybrid-powered passenger aircraft in the cruise phase. The constraints are defined as follows: ,in Including takeoff weight Landing weight Turbine front total temperature Low-pressure compressor exhaust volume and the proportion of electrical power demand These parameters correspond to the physical limitations and performance boundaries of the hybrid power system under different operating conditions.

[0093] Furthermore, the constraints are set based on a multi-dimensional response analysis of the hybrid power system when the mixture ratio changes during the cruise phase. For example, takeoff weight... and landing weight The upper and lower limits are defined by the structural strength of the passenger aircraft, its takeoff and landing performance, and airworthiness standards, and generally do not exceed the maximum permissible takeoff weight. Turbine front total temperature The upper limit is limited by the material's temperature resistance, and generally does not exceed [a certain value]. The low-pressure compressor's exhaust volume The upper limit is determined by the surge margin requirement, and typically does not exceed 10% of the low-pressure compressor inlet flow rate. Electric power demand ratio The setting is based on the maximum output capacity of the electrical system during takeoff, ensuring that the power demand during cruise does not exceed this limit in order to avoid electrical system overload.

[0094] Furthermore, the upper and lower limits of the constraints. and It is obtained through simulation analysis and fitting of experimental data, and has clear physical meaning and engineering basis. For example, and These correspond to the minimum and maximum takeoff masses of the hybrid power system under different degrees of mixing, and their values ​​are determined by a comprehensive calculation of fuel consumption, battery mass, and structural mass.

[0095] Furthermore, this constraint plays a crucial role in optimizing the mixture during the cruise phase. By setting reasonable upper and lower limits, infeasible mixture schemes can be effectively eliminated, ensuring the feasibility of the optimization results in actual flight missions. For example, in long-range missions, if an excessively high mixture causes the battery mass to exceed the limit, that mixture will be excluded from the optimization range.

[0096] Furthermore, this step introduces multi-dimensional constraints to ensure that the optimized design achieves comprehensive optimization of energy consumption, fuel consumption, and emissions while meeting airworthiness, structural safety, and system performance requirements. Its technical value lies in constructing an optimization framework that balances engineering constraints and performance objectives, providing a scientific basis for formulating operational strategies for hybrid-electric passenger aircraft.

[0097] This invention discloses a method for comprehensively optimizing the efficiency and emissions of a parallel hybrid-electric passenger aircraft. This method achieves comprehensive optimization of five indicators—battery quality, energy consumption, fuel consumption, and NOx and CO emissions—at different cruise phases, determining the optimal operating hybrid degree to improve overall energy efficiency and reduce pollutant emissions.

[0098] Example 2 This invention proposes a comprehensive optimization system for the efficiency and emissions of parallel hybrid passenger aircraft, such as... Figure 2As shown, it includes the following steps: Step 1: Define the research object. The parallel hybrid passenger aircraft being studied includes the airframe, two turbofan engines, electric propulsion system, power electronic conversion system, battery pack, and energy management and control unit. Substitute these components into Step 3. Step 2: Estimate the mass of the turbofan engine based on its overall performance parameters. The mass of the turbofan engine is calculated step by step from dry mass to wet mass. The dry mass of a single engine is expressed as:

[0099] In the formula, This indicates the dry mass of a single engine, expressed in kg. , , , These correspond to the total pressure ratio, airflow rate, bypass ratio, and thrust under the maximum takeoff thrust condition at a static ground position, respectively. The unit is kg / s. The unit is kN, and the performance parameters are shown in Table 1. According to the formula:

[0100] The dry mass of the engine is 2164.3 kg. The wet mass can be approximated as 1.56 times the dry mass. Based on this, the wet mass of a single turbofan engine can be calculated to be 3376.3 kg. Table 1. Parameters of maximum engine takeoff thrust (H = 0, Ma = 0)

[0101] Step 3: Calculate the aircraft's takeoff weight, including the turbofan engine mass, passenger and crew mass, fuel mass, electrical component mass, and other structural and component mass. The turbofan engine mass is given in Step 2. When calculating passenger and crew mass, assume an average passenger mass (including carry-on baggage) of 75 kg, an average crew mass of 85 kg, and an average weight of 14 kg per piece of checked baggage. Assuming 150 passengers, with 50% checking one extra piece of baggage, and 9 crew members, the total passenger and crew mass is 13065 kg. Fuel mass is determined by the fuel consumption requirements of the flight profile. In the calculation of the electrical component mass, since the maximum electrical power requirement in the flight mission profile occurs during takeoff, the maximum power of each electrical component is first determined based on the maximum electrical power requirement of the engines during takeoff and the efficiency of the electrical components. Then, combined with their power density, the mass of the electrical components excluding the battery is calculated to be 1088.81 kg. For the battery mass, the larger of the following two values ​​is taken: the mass calculated based on the electrical energy requirement of the flight profile, or the minimum battery mass limited by the maximum power requirement. For the mass of other structures and components, it is assumed that, without considering the electrical components of the parallel hybrid power system, the structural and equipment mass and maximum takeoff mass of the hybrid passenger aircraft are consistent with the baseline passenger aircraft, which are 32399.86 kg and 70000 kg, respectively. The takeoff weight of the passenger aircraft is the sum of the mass of the two turbofan engines, the mass of passengers and crew, the fuel mass, the mass of the electrical components, and the mass of other structures and components. Step 4: For different cruise phases, before optimizing the operational mix, based on... Figure 3 , Figure 4 , Figure 5 and Figure 6 The engine performance parameters shown (turbo front total temperature) Fuel efficiency Low-pressure compressor outlet gas discharge W bld-lpc and the proportion of electrical power required during the cruise phase to the maximum electrical power required during takeoff. The variation of the engine's air-fuel mixture during operation. As the air-fuel mixture gradually decreases during the engine's cruise phase, the total temperature before the turbine... It will increase; however, when the passenger aircraft is at its maximum required thrust (23.26 kN) during the cruise phase, and the operating mixture is 0, the total temperature before the turbine is still within the controllable range of 1560 K. Therefore, 0 (i.e., no electrical power input) is selected as the lower limit of the operating mixture during the cruise phase; as the operating mixture gradually increases during the cruise phase, the total temperature before the turbine... It will decrease, thus leading to a decrease in fuel efficiency. Significantly reduced. Simultaneously, to maintain the surge margin of the low-pressure compressor, the outlet venting volume needs to be increased. As the operating mixture increases, the venting demand increases rapidly. When the operating mixture reaches 20%, the venting volume already accounts for more than 10% of the low-pressure compressor inlet flow, severely reducing the engine's output mechanical power and lowering the overall energy utilization efficiency of the hybrid-electric aircraft. Furthermore, excessively high operating mixture will cause the required electrical power to exceed the maximum electrical power during takeoff, thus requiring an increase in the output capacity of the propulsion system's electrical components, leading to increased component mass. Therefore, the upper limit of the operating mixture during the cruise phase of the parallel hybrid-electric aircraft is set at 20%. Substitute into step 9; Step 5: Within the operating mixture range (0~20%), traverse each operating mixture with a certain step size (taking the adjustment step size of the cruise mixture as 1%) to find... Figure 7 , Figure 8 , Figure 9 , Figure 10 , Figure 11 , Figure 12 , Figure 13 , Figure 14 and Figure 15 The relative battery mass, relative energy consumption, relative fuel consumption, and relative NO shown are... x The relationship between emissions and relative CO emissions and operating mixture ratio is used to measure the impact of changes in operating mixture ratio on the converted battery mass. Energy consumption Fuel consumption Pollutant NO x Emissions CO emissions The impact of the five indicators.

[0102] relative battery quality Indicates the actual battery weight This represents the battery weight of a hybrid-electric passenger aircraft operating at zero hybrid power during the cruise phase for that range. Ratio:

[0103] relative energy consumption This indicates the energy consumption of a hybrid-electric passenger aircraft at various stages. Energy consumption of benchmark passenger aircraft at corresponding stage Ratio:

[0104] relative fuel consumption This indicates the fuel consumption of hybrid passenger aircraft at various stages. fuel consumption of benchmark passenger aircraft at corresponding stage Ratio:

[0105] Relative to NO x Emissions Indicates the NO of hybrid passenger aircraft at each stage x Emissions Phase NO of the benchmark passenger aircraft x Emissions Ratio:

[0106] relative CO emissions CO2 emissions of hybrid-electric passenger aircraft at various stages CO emissions of the benchmark passenger aircraft at the corresponding stage Ratio:

[0107] The benchmark aircraft is a conventional propulsion system aircraft that is similar to the parallel hybrid-electric aircraft under study in terms of engine performance parameters and aircraft mass.

[0108] Assume the battery mass of the hybrid passenger aircraft is... When the battery mass does not exceed the upper limit At this time, it will not have a negative impact on the aircraft's structural design, cabin layout, or ground time. Based on this, the battery mass of a hybrid-electric aircraft can be defined as the equivalent battery mass. When converting battery mass When the values ​​are equal, the impact of the battery on the aircraft's structural design, cabin layout, and ground time is considered to be consistent, and the battery mass is then converted. Represented as:

[0109] Depend on Figure 7 It is known that as the hybrid-electricity mixture increases during the cruise phase, the required electrical energy increases, leading to a rapid increase in battery mass. The longer the flight range or the higher the cruise hybrid-electricity mixture, the larger the battery capacity required for the hybrid-electric aircraft, and the greater the takeoff mass. At longer ranges, when the cruise hybrid-electricity mixture is too high, the battery mass required to complete the flight mission may exceed the limit, causing the takeoff weight to exceed the maximum takeoff mass and failing to meet the constraints.

[0110] Depend on Figure 8 , Figure 9 It can be seen that as the operational mix increases during the cruise phase, energy consumption decreases during the cruise phase. However, the energy-saving effect weakens during long-range flights due to increased takeoff weight. From the perspective of the entire flight mission, total energy consumption decreases with increasing mix during short-range flights, while energy consumption increases during long-range flights. Energy consumption for medium-range flights initially decreases and then increases. Each flight range has a cruise mix corresponding to the lowest energy consumption, which decreases as the flight range extends.

[0111] Depend on Figure 10 and Figure 11 It can be seen that as the operational mix increases during the cruise phase, the fuel consumption during the cruise phase decreases significantly. The longer the flight distance, the greater the increase in takeoff weight, and the weaker the fuel-saving effect during the cruise phase. From the perspective of the entire flight mission, when the mix is ​​low, the fuel-saving effect is more prominent for short-range missions, while for long-range missions, the cruise phase accounts for a high proportion, and the increase in mix has a greater impact on the total fuel consumption.

[0112] Depend on Figure 10 It can be seen that as the fuel mixture increases during the cruising phase, fuel consumption decreases during the cruising phase, therefore NO x Emissions were significantly reduced; from the perspective of the entire flight mission, NO x Overall emissions decrease as the mixing ratio increases.

[0113] Depend on Figure 14 , Figure 15 and Figure 10 It can be seen that as the operational mix increases during the cruise phase, CO emissions increase but the rate of increase gradually slows down and there is a peak. From the perspective of the entire flight mission, CO emissions show an initial increase followed by a decrease, and the emissions are always lower than those of the benchmark passenger aircraft.

[0114] Step 6: Determine the constraints of the five indicators mentioned in Step 5, and denote the j-th constraint that needs to be satisfied as G. j， Its lower limit and upper limit are G respectively. j,min With G j,max G1 indicates the aircraft's takeoff mass. G2 indicates the aircraft's takeoff mass. G3 indicates the total temperature before the turbine. G4 indicates the physical speed of the low-pressure shaft. G4 indicates the physical speed of the high-pressure shaft. The constraints are:

[0115] Step 7: Mark the i-th performance indicator of the hybrid-electric aircraft as... , Indicates the conversion of battery mass , Indicates energy consumption , Indicates fuel consumption , Indicates pollutant NO x Emissions , This represents the normalized CO emissions. ,for When the passenger aircraft's operating mix factor takes different values, if the constraints listed in step 6 are met, the maximum and minimum values ​​of this performance index can be obtained from step 5 as follows: and Then its corresponding normalized performance index It can be represented as:

[0116] in, This represents the normalized performance index of the i-th term. This represents the normalized converted battery mass. , Represents the normalized energy consumption , Indicates normalized fuel consumption , This indicates the normalized pollutant NO. x Emissions , This represents the normalized CO emissions. ; Step 8: Within the operational mix range determined in Step 4, find the operational mix that minimizes all performance indicators during the cruise phase under different range conditions. Based on the normalized performance indicators determined in Step 7... Calculate the normalized weights of each performance index and then perform a weighted average to form a comprehensive performance index. The comprehensive performance index is expressed as follows:

[0117] in, The weights of each performance indicator after normalization are expressed as:

[0118] in, This represents the maximum value of the i-th performance metric. and minimum value The relative decrease between them is expressed as:

[0119] The weights of the normalized performance index for different flight distances were calculated. The values ​​are as follows Figure 16 As shown, the weights for normalized conversion of battery mass. Maximum, and normalized energy consumption weight Weight of normalized CO2 emissions All are relatively small. This is mainly because as the mixture of operating conditions increases during the cruising phase, the required electrical energy rises significantly, causing a rapid increase in battery mass. Therefore, when comparing the change in the minimum value relative to the maximum value of each performance indicator, the relative change in battery mass is significantly higher than that of energy consumption, fuel consumption, and NO. xEmissions and CO emissions. In contrast, energy consumption and CO emissions have lower weights because their variation with cruising mixture ratio is limited.

[0120] Step 9: Within the operational mix range determined in Step 4, find the operational mix that minimizes all performance indicators during the cruise phase under different range conditions. The optimization objective is determined to be finding the optimal balance that minimizes the equivalent battery mass of the hybrid-electric aircraft. Energy consumption Fuel consumption Pollutant NO x Emissions CO emissions The minimum operational mixing level is used to obtain the comprehensive performance index representing the above five parameters. The minimum operational mix degree is achieved. For different flight distances, under the premise of satisfying the constraints, the operational mix degree values ​​are iterated at a certain step size (1%) to obtain the following: Figure 17 The comprehensive performance indicators shown As the operational mix changes during the cruise phase, for a specific range, the overall performance indicators decrease as the operational mix increases during the cruise phase. The aircraft first descends and then ascends, thus creating a minimum value that optimizes the overall performance of the hybrid-electric passenger aircraft. For example... Figure 18 As shown, the output makes The minimum running mixture is taken as the optimal result. This will enable comprehensive optimization of the efficiency and emissions of parallel hybrid passenger aircraft.

[0121] Example 3 To achieve the above embodiments, such as Figure 19 As shown, this embodiment also provides a comprehensive optimization device 10 for the efficiency and emissions of parallel hybrid passenger aircraft, comprising: The performance index normalization module 100 is used to establish a normalization model for five performance indicators, including battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. The dynamic weight calculation module 200 is used by [Wu Zi 3] to calculate the dynamic weight based on the relative reduction ratio of the maximum and minimum values ​​of each performance index; The constraint mixture degree traversal module 300 is used to traverse the operating mixture degree range during the cruise phase and calculate the comprehensive performance index under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume. The comprehensive performance index optimization module 400 is used to output the optimal value of the running mixture that minimizes the comprehensive performance index, thus completing the mixture optimization design.

[0122] Furthermore, the performance metric normalization module 100 is also used for: The five performance index normalization models specifically include the battery quality normalization index. Energy consumption normalization index Fuel consumption normalization index NOx emission normalization index CO emission normalization index ; The minimum and maximum values ​​of each indicator in the normalized model are determined using flight profile simulation data.

[0123] Furthermore, the dynamic weight calculation module 200 is also used for: Relative reduction ratio in dynamic weight calculation Through formula Confirmed, among which and These correspond to the minimum and maximum values ​​of each performance metric when traversing the range of mixing degrees, respectively; Weight The normalization process uses the formula This ensures that the total weight is 1 and that the priority is dynamically adjusted according to the magnitude of changes in the indicators.

[0124] This invention discloses a device for comprehensively optimizing the efficiency and emissions of a parallel hybrid passenger aircraft. It can achieve comprehensive optimization of five performance indicators of the parallel hybrid passenger aircraft, namely battery quality, energy consumption, fuel consumption, and NOx and CO emissions, under different flight conditions. It effectively balances the impact of hybridization on energy efficiency improvement and structural airworthiness, thereby improving the overall operational economy and environmental performance.

[0125] Example 4 The present invention also provides an electronic device such as Figure 20 As shown, it includes a processor and a memory. The memory stores executable instructions. When the processor executes the instructions, it implements the above-mentioned comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft.

[0126] Example 5 The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the above-described comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft.

[0127] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0128] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A comprehensive optimization method for the performance and emissions of parallel hybrid passenger aircraft, characterized in that, include: S1. Establish a normalized model that includes five performance indicators: battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. S2, calculate the dynamic weights based on the relative decrease ratio between the maximum and minimum values ​​of each performance index; S3, under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume, traverses the range of operating mixture during the cruise phase and calculates the comprehensive performance index. S4 outputs the optimal value of the running mixture that minimizes the overall performance index, thus completing the mixture optimization design.

2. The method as described in claim 1, characterized in that, S1 further includes: S11, the five performance index normalization model specifically includes battery quality normalization index Energy consumption normalization index Fuel consumption normalization index NOx emission normalization index CO emission normalization index ; S12, the minimum and maximum values ​​of each index in the normalized model are determined by flight profile simulation data.

3. The method as described in claim 1, characterized in that, S2 further includes: S21, Relative reduction ratio in dynamic weight calculation Through formula Confirmed, among which and These correspond to the minimum and maximum values ​​of each performance metric when traversing the range of mixing degrees, respectively; S22, Weight The normalization process uses the formula This ensures that the total weight is 1 and that the priority is dynamically adjusted according to the magnitude of changes in the indicators.

4. The method as described in claim 1, characterized in that, S3 further includes: S31, Takeoff weight constraints include aircraft takeoff mass and landing quality ,in It is the sum of the wet mass of the two turbofan engines, the mass of passengers and crew, the mass of fuel, the mass of electrical components, and the mass of other structures. S32, the total temperature constraint condition before the engine turbine is: The low-pressure compressor venting capacity constraint is: The inlet flow rate of the low-pressure compressor.

5. The method as described in claim 1, characterized in that, S4 further includes: S41, Run the optimal mixing value The minimum point is determined using gradient descent. When traversing the mixing degree range with a step size of 1%, the minimum point is determined using the formula. Calculate the overall performance index; S42, The optimized design results must meet the constraints. ,in This includes takeoff weight, turbine inlet total temperature, low-pressure compressor exhaust volume, and the proportion of electrical power required. .

6. A comprehensive optimization device for the efficiency and emissions of parallel hybrid passenger aircraft, characterized in that, include: The performance index normalization module is used to establish a normalization model for five performance indicators, including battery quality, energy consumption, fuel consumption, NOx emissions, and CO emissions. The dynamic weight calculation module is used to calculate the dynamic weight based on the relative decrease ratio of the maximum and minimum values ​​of each performance index of [Wu Zi 1]. The constraint mixture traversal module is used to traverse the operating mixture range during the cruise phase and calculate the comprehensive performance index under the conditions of satisfying the constraints of takeoff weight, engine turbine inlet total temperature and compressor exhaust volume. The comprehensive performance index optimization module is used to output the optimal value of the running mixture that minimizes the comprehensive performance index, thus completing the mixture optimization design.

7. The apparatus as claimed in claim 6, characterized in that, The performance index normalization module is also used for: The five performance index normalization models specifically include the battery quality normalization index. Energy consumption normalization index Fuel consumption normalization index NOx emission normalization index CO emission normalization index ; The minimum and maximum values ​​of each indicator in the normalized model are determined using flight profile simulation data.

8. The apparatus as claimed in claim 6, characterized in that, The dynamic weight calculation module is also used for: Relative reduction ratio in dynamic weight calculation Through formula Confirmed, among which and These correspond to the minimum and maximum values ​​of each performance metric when traversing the range of mixing degrees, respectively; Weight The normalization process uses the formula This ensures that the total weight is 1 and that the priority is dynamically adjusted according to the magnitude of changes in the indicators.

9. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft as described in any one of claims 1-5.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements a comprehensive optimization method for the performance and emissions of a parallel hybrid passenger aircraft as described in any one of claims 1-5.

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