To last longer in the air, fuel cell (FC) hybrid unmanned aerial vehicles (UAV) need to control the energy flow from the different power sources. In this work, multiple power sources such as photovoltaic (PV) panels, battery systems and supercapacitors (SCs) are used to supplement the FC over a flight simulation. According to the requirements of the flight scheme, a 1.1 kW proton exchange membrane fuel cell (PEMFC) system, 1kWh li-ion battery, 185W PV panel and a 1F/72V SC are considered here. To ensure minimum hydrogen consumption and degradation and a stable dc bus voltage, three energy management strategies (EMSs) are implemented and compared. A suitable topology is used, with unidirectional, bidirectional converters and their controllers.

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 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.

Li-ion batteries as a source of energy in electric vehicles (EV) and hybrid electric vehicles (HEV) are receiving more attention with the worldwide demand for energy conservation and environmental protection. In this paper, an improved State-of-Charge (SOC) co-estimation algorithm based on the second-order RC equivalent circuit model is proposed. Firstly, Forgetting Factor Recursive Least Squares (FFRLS) algorithm is adopted to realize on-line parameter identification of the model. Secondly, SOC is estimated with identified parameters by adaptive extended Kalman filter carrier tracking (AEKF) algorithm based on innovations and residuals. The results of two discharge experiments in different conditions show that the co-estimation algorithm has a higher estimation accuracy, convergence speed and robustness compared with off-line AEKF SOC estimation algorithm, which is more suitable for on-line estimation of electric vehicle SOC.

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.