Etude du multiplicateur de fréquences d'ordre cinq à base de diode à barrières quantiques

Multiplicateurs de fréquences à diodes -- Génération de fréquences -- Types de multiplicateurs de fréquences -- Diode à une ou plusieurs barrières quantiques QBV et MSQBV -- La diode varacteur à barrières quantiques -- La diode varacteur à multiples barrières quantiques MSQBV -- Conception du multiplicateur de fréquences à base de diode MSQBV À 20 GHz -- Méthode d'équilibrage harmonique -- Conditions d'optimisations -- Conception du quintupleur su MDS -- Optimisation et résultats des simulations -- Conception du filtre passe bas et de la transition micro ruban à guide d'ondes -- Filtre passe-bas -- Résultats des simulations de la transition micro ruban à guide d'ondes

Comparative Study of Online Open Circuit Voltage Estimation Techniques for State of Charge Estimation of Lithium-Ion Batteries

Online estimation techniques are extensively used to determine the parameters of various uncertain dynamic systems. In this paper, online estimation of the open-circuit voltage (OCV) of lithium-ion batteries is proposed by two different adaptive filtering methods (i.e., recursive least square, RLS, and least mean square, LMS), along with an adaptive observer. The proposed techniques use the battery’s terminal voltage and current to estimate the OCV, which is correlated to the state of charge (SOC). Experimental results highlight the effectiveness of the proposed methods in online estimation at different charge/discharge conditions and temperatures. The comparative study illustrates the advantages and limitations of each online estimation method

Optimal cost minimization strategy for fuel cell hybrid electric vehicles based on decision making framework

The low economy of fuel cell hybrid electric vehicles is a big challenge to their wide usage. A road, health, and price-conscious optimal cost minimization strategy based on decision making framework was developed to decrease their overall cost. First, an online applicable cost minimization strategy was developed to minimize the overall operating costs of vehicles including the hydrogen cost and degradation costs of fuel cell and battery. Second, a decision making framework composed of the driving pattern recognition-enabled, prognostics-enabled, and price prediction-enabled decision makings, for the first time, was built to recognize the driving pattern, estimate health states of power sources and project future prices of hydrogen and power sources. Based on these estimations, optimal equivalent cost factors were updated to reach optimal results on the overall cost and charge sustaining of battery. The effects of driving cycles, degradation states, and pricing scenarios were analyzed

An adaptive state machine based energy management strategy for a multi-stack fuel cell hybrid electric vehicle

This paper aims at designing an online energy management strategy (EMS) for a multi-stack fuel cell hybrid electric vehicle (FCHEV) to enhance the fuel economy as well as the fuel cell stacks (FCSs) lifetime. In this respect, a two-layer strategy is proposed to share the power among four FCSs and a battery pack. The first layer (local to each FCS) is held solely responsible for constantly determining the real maximum power and efficiency of each stack since the operating conditions variation and ageing noticeably influence stacks' performance. This layer is composed of a FCS semi-empirical model and a Kalman filter. The utilized filter updates the FCS model parameters to compensate for the FCSs' performance drifts. The second layer (global management) is held accountable for splitting the power among components. This layer uses two inputs per each FCS, updated maximum power and efficiency, as well as the battery state of charge (SOC) and powertrain demanded power to perform the power sharing. The proposed EMS, called adaptive state machine strategy, employs the first two inputs to sort the FCSs out and the other inputs to do the power allocation. The ultimate results of the suggested strategy are compared with two commonly used power sharing methods, namely Daisy Chain and Equal Distribution. The results of the suggested EMS indicate promising improvement in the overall performance of the system. The performance validation is conducted on a developed test bench by means of hardware-in-the-loop (HIL) technique

Online energy management of a hybrid fuel cell vehicle considering the performance variation of the power sources

This study investigates the impact of battery and fuel cell (FC) degradation on energy management of a FC hybrid electric vehicle. In this respect, an online energy management strategy (EMS) is proposed considering simultaneous online adaptation of battery and FC models. The EMS is based on quadratic programming which is integrated into an online battery and proton exchange membrane FC (PEMFC) parameters identification. Considering the battery and PEMFC states of health, three scenarios have been considered for the EMS purpose, and the performance of the proposed EMS has been examined under two driving cycles. Numerous test scenarios using standard driving cycles reveal that the ageing of battery and PEMFC has a considerable impact on the hydrogen consumption. Moreover, the proposed EMS can successfully tackle the model uncertainties owing to the performance drifts of the power sources at the mentioned scenarios

Efficient power-electronic converters for electric vehicle applications

This paper introduces advanced power-electronic converter topologies for Electric Vehicles (EVs) using a four-phase DC/DC interleaved boost converter (FP-IBC) and a five-level T-type DC/AC multilevel converter. A comparison between the proposed topologies and other converter topologies is performed and discussed. The simulation results are analysed to evaluate the converters based on power loss calculations and harmonic analysis. The converters are studied at different switching frequencies and various loading conditions to reflect their effects on the converter losses. The results highlight the proposed converters' higher efficiency compared to other studied converter topologies in electric vehicle applications

Adaptive control of four-quadrant DC-DC converters in both discontinuous and continuous conduction modes

The inherently different dynamics of a DC-DC converter while operating in both continuous conduction mode (CCM) and discontinuous conduction mode (DCM) necessitate an advanced controller to control the inductor current. A conventional PI controller cannot be used across both modes since it does not guarantee a smooth transition between both modes. Furthermore, in time-varying input-output voltage applications of the four-quadrant converter such as in battery charging applications, the location of the boundary between the CCM and the DCM changes dynamically, creating an uncertainty. Therefore, a robust controller is required to accurately track the inductor current in the presence of uncertainties. Thus, an adaptive controller is proposed in this work, which is based on the general inverse model of the four-quadrant converter in both modes. Moreover, gain scheduling is used to switch the parameters of the controller as the converter transits between the DCM and the CCM. The adaptability and effectiveness of the controller in ensuring a smooth transition is validated by numerical simulations conducted on various converter topologies. Experimental results are also presented for a buck converter