Hybrid electric vehicles (HEVs) and pure electric vehicles (EVs) rely on energy storage devices (ESDs) and power electronic converters, where efficient energy management is essential. In this context, this work addresses a possible EV configuration based on supercapacitors (SCs) and batteries to provide reliable and fast energy transfer. Power flow among the aforementioned energy sources and the EV occurs through a dc link associated with an interleaved bidirectional dc–dc converter. This topology presents low component count and high efficiency over a wide load range, consisting of an adequate choice for high-power, high-current levels. A detailed design methodology is presented, which comprises the qualitative and quantitative analysis of the converter and the control system implementation using a simple strategy, i.e., average current mode control. A 2 kW laboratory prototype of the converter using microcontroller dsPIC30f4011 by Microchip is implemented and the system performance is evaluated in terms of some key waveforms. High power density, low component count and increased robustness can be stated as the main advantages of the proposed approach, which can be considered as a viable alternative for practical EV applications.

Interleaved bidirectional DC–DC converter for electric vehicle applications based on multiple energy storage devices

Transformational techniques unifying synthesis of two-state DC-DC converters and analytical synthesis techniques allowing generation of all possible converters meeting a certain criteria already exist. The analysis of a family of converters derived from a single converter cell has also been unified. Current waveforms generated by the family of converters were shown to be related. However, a concept or basic building blocks that facilitate unified synthesis, analysis, prediction of current waveforms and assignment of switch states over a very wide range of DC-DC converters is still lacking. This study will propose three 3-terminal basic building blocks and one 3-terminal filter block. It will be shown that between them, they are sufficient for realizing all non-isolated DC-DC converters excluding those with coupled inductors. The various DC-DC converters fall into those realized through cascade, stacked, stacked plus cascade, interleaved/paralleled or differential connection of the basic building blocks. A systematic approach for evaluating input-output current gains will be presented. Moreover, a basic building block will be shown to have fixed switching states for proper operation. This gives rise to the generation of a unique set of current waveforms at the three terminals irrespective of where a basic building block is embedded. It has been shown that the effort and time needed to design DC-DC converters can be reduced as switching device stresses can be estimated without the need for tedious first principle derivations.

/pdf/unified-approach-for-synthesis-and-analysis-of-non-isolated-1xempprp8q.pdf

Unified Approach for Synthesis and Analysis of Non-Isolated DC-DC Converters

This article presents an offline deep learning approach focused to model and identify a 270- to 28-V dc–dc step-down converter used in on-board distribution systems of more electric aircrafts (MEAs). Manufacturers usually do not provide enough information of the converters. Thus, it is difficult to perform design and planning tasks and to check the behavior of the power distribution system without an accurate model. This work considers the converter as a black box and trains a wavelet convolutional neural network (WCNN) that is able to accurately reproduce the behavior of the dc–dc converter from a large set of experimental data. The methodology to design a WCNN based on the characteristics of the input and output signals of the converter is also described. The method is validated with the experimental data obtained from a setup that replicates the 28-V on-board distribution system of an aircraft. The results presented in this article show a high correlation between the measured and estimated data, robustness, and low computational burden. This article also compares the proposed approach against other techniques presented in the literature. It is possible to extend this method to other dc–dc converters, depending on their requirements.

Black-Box Modeling of DC–DC Converters Based on Wavelet Convolutional Neural Networks

This paper presents an intelligent voltage controller designed on the basis of an adaptive neuro-fuzzy inference system (ANFIS) for a flyback converter (FC) working in continuous conduction mode (CCM). The union of fuzzy logic (FL) and adaptive neural networks (ANN) makes ANFIS more robust against model parameters’ uncertainties and perturbations in input voltage or load current. ANFIS inherits the advantages of structured knowledge representation from FL and learning capability from NN. Comparative analysis showed that the ANFIS controller offers not only the superior transient response characteristics, but also excellent steady-state characteristics compared to those of the FL controller (FLC) and proportional–integral–derivative (PID) controllers, thus validating its superiority over these traditional controllers. For this purpose, MATLAB/Simulink environment-based simulation results are presented for validation of the proposed converter compensated system under all operating conditions.

Artificial Intelligence-Based Controller for DC-DC Flyback Converter

Utilization of renewable energies in association with energy storage is increased in different applications such as electrical vehicles (EVs), electric boats (EBs), and smart grids. A robust controller strategy plays a significant role to optimally utilize the energy resources available in a power system. In this paper, a suitable controller for the energy resources of an EB which consists of a 5 kW solar power plant, 5 kW fuel cell, and 2 kW battery package is designed based on the linear parameter varying (LPV) controller design approach. Initially, all component dynamics are augmented, and by exploiting the sector-nonlinearity approach, the LPV representation is derived. Then, the LPV control method determines the suitable gains of the states’ feedbacks to provide the required pulse commands of the boost converters of the energy resources to regulate the DC-link voltage and supply the power of EB loads. Comparing with the state-of-the-art nonlinear control methods, the developed control approach assures the stability of the overall system, as it considers all component dynamics in the design procedure. The real-time simulation results demonstrate the performance of the designed controller in the creation of a constant DC-link voltage.

/pdf/a-linear-parameter-varying-control-approach-for-dc-dc-58qi5q38y4.pdf

A Linear Parameter Varying Control Approach for DC/DC Converters in All-Electric Boats

Modeling, design and control of a DC-DC converter using state-space averaging approached are presented. A proposed DC-DC converter topology is based on two half-bridge DC-DC converters which control the power between the battery, energy storage system (ESS) of an ultra-capacitor, and an output load resistance. Two well-known control schemes based on AC equivalent circuit modeling are used to design converter control system. The average voltage mode control is used for the boost converter operation and the two-loop average current mode control for the bidirectional operation. Control schemes are modeled and designed, the performance and responses were analyzed using Matlab/Simulink. The simulation results show that the proposed control system is capable to regulate efficiently the power flow between two DC sources.

M. Shahriman M. Sarif

Papers

Modeling, design and control of bidirectional DC-DC converter using state-space average model