Browsing by Author "Xie, Ye"

Now showing 1 - 9 of 9
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    Convexification in energy optimization of a hybrid electric propulsion system for aerial vehicles
    (Elsevier, 2022-03-30) Xie, Ye; He, Shaoming; Savvaris, Al; Tsourdos, Antonios; Zhang, Dan; Xie, Anhuan
    This paper concerns the energy management of a hybrid electric propulsion system for aerial vehicles, using convex optimization. The main contribution of this paper is the proposal of a new convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and the convexified problem. The primary energy management is formulated from first principles and using experimental data. The convexity of the original problem is clarified via investigating the approximation to the experimental data. Then, change of variables and equality relaxation are implemented to convexify the concave constraints. The introduced variable—battery internal energy, is proposed to convexify the battery model. The relaxation of a non-affine equality yields to new convex inequality constraints. Numerical examples and forward simulations were carried out to validate the convexified problem. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with less fluctuation of the optimal control value. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application.
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    Design and energy management of aircraft hybrid electric propulsion system.
    (2018-09) Xie, Ye; Savvaris, Al; Tsourdos, Antonios
    This thesis investigates the design and development of a Hybrid Electric Propulsion System (HEPS) for aircraft. The main contributions of the study are the multi-objective system sizing and the two energy optimization algorithms. First, the system sizing method is employed to design the hybrid electric propulsion system for a prototype aircraft. The sized hybrid propulsion system can ensure that no significant performance is sacrificed and the fuel economy is improved. The novel approach in this work is a new non-dominated sorting algorithm for the Non-dominated Sorting Genetic Algorithm (NSGA). The new algorithm can improve the time complexity of non-dominated sorting process. The optimized hybrid aircraft can save up to 17% fuel, achieve higher cruising speed and rate of climb. It is concluded that the optimal results are more sensitive to the variation of battery energy density than other parameters. Next, the main components of the HEPS are modelled for example. The engine model provides an insight into the inherent relationship between the throttle command and the output torque. Regarding the d-q model of motor/generator, the estimation of torque loss at steady state is achieved using the efficiency map from experiments. The application of Shepherd model leads to the straightforward parameter identification. In this research, both non-causal and causal energy management strategies for HEPS are investigated. The main novelty when studying convex optimization is the proposal of a new lossless convexification, which simplifies the formation of the convexified problem, and the proof of equality between the original problem and convexified problem. The introduced variable—battery internal energy, is proposed to convexify the battery model. The first test case verifies that the convex relaxation does not sacrifice the optimality of the solution nor does the variable change lose the original bounds. Also, the optimal control from convex optimization is demonstrated to be robust to a disturbance in power demand. Comparison with the benchmark optimization—dynamic programming, shows that convex optimization achieves a minimal objective value with much less optimization time. Most significant is that the convexification reduces the optimization computation time to a level compatible with implementation in practical application. In causal control, the main focus is to extend the original Equivalent Consumption Minimization Strategy (ECMS) with the fuzzy control. The proposed algorithm can maintain the battery State of Charge (SoC) in a desirable range, without the requirement of off-line estimation of equivalence factor. By comparing with non-causal control—dynamic programming, the test cases validates that the fuzzy based ECMS succeeds in converting the non-causal optimization, with little sacrifice of the optimality of the solution. In other words, the prior-knowledge of flight mission is not a pre- requisite, and the fuzzy based ECMS can achieve the sub-optimal control for on-line implementation. The fuzzy based ECMS is also validated to outperform the adaptive ECMS, since it can reduce the computation time of optimization and save more fuel usage. The theoretical relationship between the equivalence factor of ECMS and the co-state variable of Hamiltonian function is also demonstrated in this thesis. The convex optimization and fuzzy based ECMS are combined to complete a flight mission with several sub-tasks. Each task has different power and SoC requirements. The test case demonstrates that only the combination of non-causal and causal optimization can satisfy the various constraints and requests of the test scenario. Compared with the engine-only powered aircraft, the hybrid powered aircraft saves 18.7% on fuel consumption. Furthermore, the hybrid propulsion system has better efficiency since it integrates the high efficient electric powertrain.
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    Development of a fuel cell hybrid-powered unmanned aerial vehicle
    (IEEE, 2016-08-08) Savvaris, Al; Xie, Ye; Malandrakis, Konstantinos; Tsourdos, Antonios
    This paper describes the design and development of a hybrid fuel cell/battery propulsion system for a long endurance small UAV. The high level system architecture is presented, followed by the hardware-in-the-loop testing and performance analysis. A high fidelity 6-DoF simulation model of the complete system was developed and used to test the system under different battery state-of-charge. The simulation model included the power manager for the hybrid propulsion system configuration, which is based on rule-based control. The simulation results are compared with the experimental results obtained from the Hardware-in-the-Loop testing.
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    Fuzzy logic based equivalent consumption optimization of a hybrid electric propulsion system for unmanned aerial vehicles
    (Elsevier, 2018-12-07) Xie, Ye; Savvaris, Al; Tsourdos, Antonios
    This paper presents an energy management strategy for a hybrid electric propulsion system designed for unmanned aerial vehicles. The proposed method combines the Equivalent Consumption Minimization Strategy (ECMS) and fuzzy logic control, thereby being named Fuzzy based ECMS (F-ECMS). F-ECMS can solve the issue that the conventional ECMS cannot sustain the battery state-of-charge for on-line applications. Furthermore, F-ECMS considers the aircraft safety and guarantees the aircraft landing using the remaining electrical energy if the engine fails. The main contribution of the paper is to solve the deficiencies of ECMS and take into consideration the aircraft safely landing, by implementing F-ECMS. Compared with the combustion propulsion system, the hybrid propulsion system with F-ECMS at least reduces 11% fuel consumption for designed flight missions. The advantages of F-ECMS are further investigated by comparison with the conventional ECMS, dynamic programming and adaptive ECMS. In contrast with ECMS and dynamic programming, F-ECMS can accomplish a balance between sustaining the battery state-of-charge and electric energy consumption. F-ECMS is also superior to the adaptive ECMS because there are less fuel consumption and lower computational cost.
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    Modelling and control of a hybrid electric propulsion system for unmanned aerial vehicles
    (IEEE, 2018-06-28) Xie, Ye; Savvaris, Al; Tsourdos, Antonios; Laycock, Jason; Farmer, Andrew
    This paper presents the modelling and control of a hybrid electric propulsion system designed for unmanned aerial vehicles. The work is carried out as part of the AIRSTART project in collaboration with Rotron Power Ltd. Firstly, the entire parallel hybrid powertrain is divided into two powertrains to facilitate the modelling and control. Following this, an engine model is built to predict the dynamics between the throttle request and the resulting output. It is then validated by comparing with experimental data. On the basis of d-q model of the motor/generator, a good estimation of torque loss at steady state is achieved using the efficiency map. Next, a rule-based controller is designed to achieve the best fuel consumption by regulating the engine to operating around its ideal operating line. Following the integration of the models and controller, the component behaviour and control logic are verified via the final simulation. By enabling the engine to operate at its best fuel economy condition, the hybrid propulsion system developed in this research can save at least 7% on fuel consumption when compared with an internal combustion engine powered aircraft.
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    Probabilistic Monte-Carlo method for modelling and prediction of electronics component life
    (SAI Organization, 2014-01-31) Sreenuch, T.; Alghassi, Alireza; Perinpanayagam, Suresh; Xie, Ye
    Power electronics are widely used in electric vehicles, railway locomotive and new generation aircrafts. Reliability of these components directly affect the reliability and performance of these vehicular platforms. In recent years, several research work about reliability, failure mode and aging analysis have been extensively carried out. There is a need for an efficient algorithm able to predict the life of power electronics component. In this paper, a probabilistic Monte-Carlo framework is developed and applied to predict remaining useful life of a component. Probability distributions are used to model the component’s degradation process. The modelling parameters are learned using Maximum Likelihood Estimation. The prognostic is carried out by the mean of simulation in this paper. Monte-Carlo simulation is used to propagate multiple possible degradation paths based on the current health state of the component. The remaining useful life and confident bounds are calculated by estimating mean, median and percentile descriptive statistics of the simulated degradation paths. Results from different probabilistic models are compared and their prognostic performances are evaluated.
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    Review of hybrid electric powered aircraft, its conceptual design and energy management methodologies
    (Elsevier, 2020-08-13) Xie, Ye; Savvaris, Al; Tsourdos, Antonios; Zhang, Dan; Gu, Jason
    The paper overviews the state-of-art of aircraft powered by hybrid electric propulsion systems. The research status of the design and energy management of hybrid aircraft and hybrid propulsion systems are further reviewed. The first contribution of the review is to demonstrate that, in the context of relatively underdeveloped electrical storage technologies, the study of mid-scale hybrid aircraft can contribute the most to both theoretical and practical knowledge. Meanwhile, the profits and potential drawbacks of applying hybrid propulsion to mid-scale hybrid airplanes have not been thoroughly illustrated. Secondly, as summed in the overview of design methodologies, the multi-objective optimization transcends the single-objective one. The potential of the hybrid propulsion system can be thoroughly evaluated in only one optimization run, if several objectives optimized simultaneously. Yet there are few researches covering the conceptual design of hybrid aircraft using multi-objective optimization. The review of the most popular energy management strategies discloses the third research gap—current methodologies favoured in hybrid ground vehicles do not consider the aircraft safety. Additionally, both non-causal and causal energy management are needed for performing a complicated flight mission with several sub-tasks.
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    Sizing of hybrid electric propulsion system for retrofitting a mid-scale aircraft using non-dominated sorting genetic algorithm
    (2018-09-24) Xie, Ye; Savvaris, Al; Tsourdos, Antonios
    The paper presents the sizing of a hybrid electric propulsion system for a prototype aircraft. The main contribution of the paper is to apply multi-objective optimization in the retrofit of a mid-scale aircraft and investigate the fuel economy of the hybrid aircraft for a particular mission cycle. Using the Non-dominated Sorting Genetic Algorithm (NSGA), the fuel consumptions for different flight durations are minimized, which represent the optimal trade-off between fuel consumption and flight duration. With no compromise on endurance and range, the maximum fuel reduction of the retrofitted hybrid aircraft reaches 17.6%, by comparison with the prototype aircraft. The retrofitted aircraft achieves better cruising and climbing performance with the sized hybrid propulsion system. The novelty of the study is the proposal of a new non-dominated sorting algorithm—Benchmark based Non-Dominated Sort (BNDS) for the NSGA. BNDS can reduce the number of comparisons and the time complexity of the non-dominated sorting process. A constraint handling approach is also integrated into the BNDS/NSGA to address performance and mission requirements.
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    Trajectory optimization for multi-sensor multi-target search and tracking with bearing-only measurements
    (MDPI, 2023-07-20) Yang, Xiwen; Yin, Hang; He, Shaoming; Xie, Ye; Shin, Hyosang
    This paper proposes a trajectory optimization approach for multi-sensor multi-target search and tracking using bearing-only sensors. Based on the framework of the joint integrated probabilistic data association (JIPDA) filter, the intensity of potential unknown targets is updated according to the trajectories of the UAVs. The performance indices for target search and tracking are constructed based on, respectively, the intensity of unknown targets in the search area and the tracking error covariance. A dimensionless criterion, evaluating the search and tracking performance, is formulated and leveraged as the objective function of the UAV trajectory optimization problem. Simulations were carried out in different search and tracking scenarios to demonstrate the effectiveness of the proposed approach.