An Energy Efficient Power Management Solution for a Fault-Tolerant More Electric Engine/Aircraft

Zhang, Yicheng; Peng, Gabriel Ooi Heo; Banda, Joseph Kiran; Dasgupta, Soura; Husband, Mark; Su, Rong; Chen, Wen

doi:10.1109/tie.2018.2877169

Cited by 35 publications

(21 citation statements)

References 19 publications

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“…In this regard, it is necessary to pay attention not only to lithium-ion batteries, but also to promising developments-metal-air batteries, that have a lower mass and smaller size with a greater potential energy density. [20][21][22].…”

Section: Discussionmentioning

confidence: 99%

Features of the choice of power unit for electric tricycle

Stakhin

Goncharov

2019

E3S Web Conf.

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The article considers the bolide Shell Eco Marathon, which main operating purpose is to cover the maximum distance with the minimum fuel consumption. With the aim of providing the bolide with technical and operational indicators, it is appropriate to solve the problem of decreasing the bolide’s mass, the air drag coefficient, and also the choice of the economic power plant. The basic formulas for the engine traction-dynamic calculation are presented. Due to the results of the calculation, the conclusion about profitability of the power plant is made. There is also a description of the principle of the electric engine and its main advantages comparing to the internal combustion engines.

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Section: Discussionmentioning

confidence: 99%

Features of the choice of power unit for electric tricycle

Stakhin

Goncharov

2019

E3S Web Conf.

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“…14 (10). X and Y in (17) and (18) represent the bound constraints by (12). The proposed algorithm is given as follows: 1) x * 0 is defined to be the optimal solution for the following problem:…”

Section: A Outer-layermentioning

confidence: 99%

“…These works finally verifies its performance with different levels of thrust and electric power demands. In contrast, the papers in [16]- [18] provide a power management/control model to co-optimize the energy output of the more-electric turbofan engine and electric loads. The above approaches for energy optimization analysis are novel but have the following challenges: 1) fixed electric loads provided by turbofan engine are assumed, i.e.…”

Section: Introductionmentioning

confidence: 99%

Energy Optimization Analysis of Turbofan Engine for More-Electric Civil Aircraft

Yang

Li²,

Chen³

2020

IEEE Access

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The civil aviation industry is moving into the more-electric environment where the civil aircraft uses electricity to meet the multiple energy demands of the associated aircraft subsystems. The civil aircraft's turbofan engine, which is the largest energy supply system of civil aircraft, will thus utilize more fuel resource to provide increased electric energy besides of its conventional responsibility of maintaining the fundamental thrust requirements by aircraft. This will introduce new challenge for the energy optimization analysis of aircraft turbofan engine: it was nearly a simple optimal setting of engine's component parameters for keeping required thrust, but under the more-electric environment it will become an optimization problem in order to minimize the fuel consumption while obeying the multiple constraints by thermodynamic limits of turbofan engine and by varying electric power demands associated with the flight profile of aircraft. We present a complete modeling in this paper for the energy optimization analysis of turbofan engine for more-electric civil aircraft and formulate it as a nonlinear programming form. We propose an algorithm based on Benders decomposition method to solve this problem; the numerical results demonstrate the economic effectiveness of the proposed modeling and algorithm. INDEX TERMS More-electric civil aircraft, turbofan engine, energy optimization analysis, decomposition method, nonlinear optimization.

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“…Step 2: with the Grey modeling mechanism based on moving window, GM(1, 1) can be established to predict the IDI series segments. According to equations (11) to (13), the coefficients, i.e., a g and b g , can be obtained.…”

Section: Improved Grey-markov Model With Moving Windowmentioning

confidence: 99%

“…roughout the entire process of prediction, an appropriate indicator needs to be constructed to quantify the degradation levels of an aircraft engine, and it can be regarded as the foundation of the subsequent degradation trend forecasting. With the accumulation of running time, large amounts of sensory data from different positions are collected for analysis [13,14]. On this basis, how to build a degradation indicator by adequately utilizing these data is the primary problem to be solved in the task of tendency prediction.…”

Section: Introductionmentioning

confidence: 99%

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

et al. 2019

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As an essential component and core power source of aircraft, the operational stability of aeroengine has important impact on system safety and reliability. Accurate degradation tendency prediction on an engine can not only improve its operational stability but also significantly reduce the maintenance costs. In this paper, a novel forecasting method that combines CEEMDAN permutation entropy and improved Grey–Markov model is proposed to perform multistep degradation tendency prediction of aircraft engines. In order to accurately quantify the degradation level of engines, a new integrated degradation index (IDI) is innovatively designed by multidimensional sensory data. And then, because of high speed and excellent performance, CEEMDAN algorithm is specifically employed to decompose the generated IDI series to eliminate the potential influence of stochastic fluctuations. Aiming at the complexity of intrinsic mode functions (IMFs) generated by CEEMDAN, an IMFs reconstruction strategy based on permutation entropy is developed to better characterize the degradation states. Finally, on the basis of above achievements and for higher forecasting efficiency and accuracy, an improved Grey–Markov model combined with the moving window algorithm, which is unique, is constructed to realize multistep degradation trend prediction of engines. The proposed method is applied to the degradation tendency prediction of aircraft engines. The experimental results validate the effectiveness and superiority of the proposed method, and it is more suitable for engineering applications in comparison with other methods.

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An Energy Efficient Power Management Solution for a Fault-Tolerant More Electric Engine/Aircraft

Cited by 35 publications

References 19 publications

Features of the choice of power unit for electric tricycle

Features of the choice of power unit for electric tricycle

Energy Optimization Analysis of Turbofan Engine for More-Electric Civil Aircraft

Multistep Degradation Tendency Prediction for Aircraft Engines Based on CEEMDAN Permutation Entropy and Improved Grey–Markov Model

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