Capacity configuration optimization is one of the primary tasks in the construction of the integrated energy system (IES). the uncertainties of renewable energy sources and loads deepen the coupling relationship between the capacity configuration and energy dispatching of IES, which makes optimizing the capacity difficult. This paper proposes a two stage GA-MILP-based capacity sizing allocation method for off- grid IES. Firstly, a double-layer capacity configuration framework based on Leader-Follower is constructed. Secondly, the genetic algorithm (GA) is used to randomly generate the initial configuration in the Leader layer and use it as the input of the Follower layer. In the Follower layer, the system investment cost, operation cost, and maintenance cost are comprehensively considered, the mixed integer linear programming (MILP) method is used to obtain the minimum operating cost and energy dispatching scheme and then return to the Leader layer. The optimal capacity configuration scheme is obtained through multiple iterative optimizations. Finally, the proposed method is applied to an Off-grid island to confirm the rationality and effectiveness of the optimization results.

There are many disturbances and uncertainties in the DC microgrid system. Therefore, it is required that the converter control strategy has strong robustness. Active disturbance rejection control (ADRC) has been proven to be a very effective method. However, the rejection of time-varying disturbance by traditional ADRC needs to be improved. To this end, first, a universal expression for the output characteristics of ADRC with different disturbance observers is provided in this paper, which facilitates the comparison of performance between ADRCs. Then two kinds of ADRCs based on higher-order extended state observer (HESO) and cascaded extended state observer (CESO) are studied to improve the disturbance rejection characteristics of the converter. Finally, the effectiveness of the proposed controller is verified by the simulation results.

Energy Management Strategy (EMS) is a crucial part of More-Electric Aircraft (MEA) and aims at improving the efficiency of whole hybrid energy system. In this paper, fuzzy neural network trained by dynamic programming (FNDP) is proposed to solve the problem that dynamic programming (DP) cannot be used online. FNDP can obtain the optimal distribution scheme using DP and extract the rule from the data through fuzzy neural network (FNN). Compared to fuzzy control strategy based on power follow (PFF) in two different load profiles, it can be found that FNDP can attain a satisfied rule without manual setting and have a better performance than PFF.

In this paper, the hybrid power system of fuel cell Unmanned Aerial Vehicle (UAV) is established on the platform of Matlab/Simulink whose system architecture is fuel cell with lithium battery, and the overall scheme of the system is designed. In addition, the required power curves under two typical working conditions are obtained by establishing the aircraft dynamics model. An optimization energy management strategy based on dynamic programming is designed to achieve the least hydrogen consumption. For comparison, another two energy management strategies are designed under the same working condition. Finally, it is proved that dynamic programming has optimal fuel economy than the other two strategies. Moreover, each strategy has its own characteristics, which provides suggestions for selection of energy management strategies under different objectives.

In order to mitigate the environmental pollution problem, the fuel cell electric vehicle (FCEV), as one kind of renewable energy industry, is researched and developed. The performance of FCEV is deeply relied on the energy management strategy (EMS), an inappropriate EMS may cause the bad performance of FCEV. Strategies designed under specific work condition have poor adaptability to complex work conditions. What’s more, these strategies give little concern on the volatility of fuel cell’s output power, which will cause a loss of fuel cell’s life. Aiming at decreasing the hydrogen consumption of FCEV and the volatility of the output power of fuel cell, an optimized fuzzy logic control energy management strategy based on work condition recognition is proposed. In order to achieve a better performance, this strategy can recognize instant driving condition, and pick the corresponding parameters of fuzzy logic control system from the preset database of the parameters. The database is obtained by offline optimization with genetic algorithm. A simulation result based on the MATLAB/Simulink platform is obtained to demonstrate that this strategy has a better performance than a conventional fuzzy logic controller with fixed parameters under mixed work conditions.

This paper presents an observer based power switch open-circuit fault diagnosis method for a two-phase interleaved boost converter (IBC). Motivated by the simplicity and robustness of immersion and invariant (I&I) theory, I&I observers are adopted for residual generation. The inductor current and output voltage used in controllers are selected for input voltage estimation, which avoids the use of extra sensors. Furtherly, to eliminate the unexpected effects, which are caused by parameter uncertainty, an adaptive threshold is designed. The simulation results obtained under different operation situations have demonstrated that the preselected threshold has a strong robustness against internal and external disturbances with no false alarms. What' more, the simulation results also show that the switch open-circuit diagnosis is equal to one switch period under various conditions. Thus, the effectiveness of the proposed diagnosis method is validated rigorously.

A novel control scheme for two phases floating interleaved boost DC-DC converter (FIBC) is presented in this paper. The proposed controller is based on second order sliding mode method combined with an extend state observer (ESO), which comprises two loops: the outer loop is a voltage regulation loop whereas the inner loop is an instantaneous current regulation loop. The outer loop is accomplished by SOSM and ESO, which aims to regulate the output voltage of the converter and reject the disturbance of the load variations. The SOSM strategy is also applied to inner loop to make the current track its reference accurately. Through the proposed approach, the system can obtain a stronger robustness, and the chattering problem is also suppressed. Both theoretical analysis and simulation results are demonstrated. Moreover, in order to verify the effectiveness of the proposed control scheme, comparisons with dual loops super-twisting control scheme (ST + ST) and outer active disturbance reject control plus inner super-twisting control scheme (ADRC + ST) in case of parameter uncertainties and loads disturbances have been made in this paper.

This paper aims to the design and control of a de-de floating interleaved boost converter with high voltage gain. Firstly, the process of the components sizing is presented in detail. Secondly, the controller with dual-loop cascade structure, both based on super-twisting sliding mode algorithm, are proposed to deal with the uncertainties of the converter. The control goals of constant converter output voltage, equal current sharing between phases and constant switching frequency (for phase interleaving to reduce the input current ripple) are achieved. The stability of the sliding mode inner loop and the outer loop are both proven using Lyapunov stability theorem. The effectiveness and the robustness of the proposed controller are validated by the simulation and experiment results.

Owing to the energy shortage and the increasingly serious environmental pollution, fuel cell electric vehicles (FCEV) with zero-emission and high-efficiency have been expected to be the most potential candidate to substitute the conventional vehicles. The DC/DC converter is the interface between the fuel cell (FC) and the driveline of FCEV. It not only needs high voltage gain to convert the wide FC voltage into an appropriate voltage level, but also needs the capacity of fault tolerance to enhance the reliability of the system. For this reason, floating interleaved boost converters (FIBC) seem to be the optimal selection. Despite this topology can continue operating without interruption under the action of the proper control scheme in the case of power switch open circuit fault (OCF), operating in degraded mode has adverse impacts on the component stress and the input current ripple. Hence, this paper aims to design an effective controller to maintain the dc bus voltage constant and to demonstrate thorough theoretical analysis and simulation verification of these undesirable effects.

This paper aims to focus on the smooth output of Buck converters under the input perturbations and load disturbances using the high-order sliding mode controller with a differentiator (HOSMD) based on the only measurement of output voltage. Theoretical analysis and design procedures of the controller designed by prescribed control law, one of the high-order sliding modes, are presented in detail. As well as the differentiator with the super-twisting. Comparison of numerical simulation results between traditional sliding mode control (SMC) and HOSMD under steady state, input voltage perturbations, output load disturbances have been presented. And it validates the effectiveness and robustness of the method proposed in this paper, which would reduce the hard-ware cost and increase the superiority.