This paper introduces a highly efficient bidirectional resonant dc/dc converter over wide range of battery voltages for vehicle-to-grid (V2G) capable electric vehicles (EVs). It operates as a pulse-width-modulation (PWM) full-bridge series-resonant converter in the forward direction and a half-bridge resonant boost converter in the backward direction. One advantage of the proposed converter is that it has wide voltage gain range in the backward operation. Also, it requires only six active switches. To achieve high efficiency, SiC MOSFETs are used for two bottom switches in the primary side, because only these switches suffer hard switching turn-off in both forward and backward directions. Since it operates with fixed-frequency and with PWM control, the magnetic components and passive filters can be optimally designed with respect to the volume and the loss. Thus, the proposed converter achieves low cost, high conversion ratio, and high efficiency over wide range of battery voltages. Detailed analysis of the converter operation is presented along with the design procedure. A 3.3 kW/400 V prototype of the proposed converter has been built to operate for 250-415 V primary source voltages and tested to demonstrate its circuit design.

This paper proposes a repetitive controller with phase-lead compensation for an unfolding-type Cuk continuous conduction mode (CCM) inverter operating in the CCM. The Cuk CCM inverter features medium power capacity, step up/down ability, and low input/output current ripples, so it is well suited to distributed power generation systems. To achieve accurate tracking of the reference output current, we make the use of the repetitive controller coupled with a conventional proportional-integral controller and a nominal duty ratio. In developing the proposed controller, we use the average model of the Cuk CCM inverter in the grid-connected case. The two right-half plane (RHP) zeros in the transfer function of the Cuk CCM inverter cause phase lag of the closed-loop system; to compensate for the phase lag, we implement a phase-lead compensation algorithm in the repetitive control scheme. We also provide detailed and practical design guidelines of the control parameters to develop a stable Cuk CCM inverter. Experimental tests using a 500-W Cuk CCM inverter demonstrate the desirable performance of the proposed control approach.

In this paper, we propose a high step-up resonant dc-dc converter with ripple-free input current for renewable energy systems. We use an input-current doubler and a switching mechanism employed at an output-voltage doubler to achieve high step-up voltage gain without having to use a transformer with high turns ratio. An active-clamp circuit installed on the primary side suppresses the surge voltage at the switch components and recycles the energy stored in the leakage inductance. A resonance that occurs at the secondary side of the converter is used to reduce the turn-off current and switching loss significantly, and to achieve high power conversion efficiency. The input-current ripple declines to zero theoretically because the duty cycle of the primary-side switches is always set to 0.5 regardless of the input voltages and load variations. The circuit operations, steady-state analysis, and design guideline of the proposed converter are also presented. A 600-W prototype converter has been fabricated to demonstrate the performance of the proposed converter.

This article presents a bidirectional soft-switching push-pull resonant converter that is highly efficient over a wide range of battery voltages. It is derived by integrating a current-fed push-pull circuit and an active voltage doubler circuit with a bidirectional switch. The converter operates as a pulsewidth modulation (PWM) current-fed push-pull resonant-boost converter in the forward direction, but as a PWM half-bridge resonant converter in the backward direction. The most attractive feature of the proposed converter is that, by employing the bidirectional switch and corresponding switching modulation, it can achieve almost ZVS at turn-off instants under wide variations of battery voltages. Therefore, the proposed converter achieves high efficiency even during high-frequency operation. Moreover, no instantaneous reactive current flows through the circuit under wide variations of battery voltages and loads. Furthermore, the proposed bidirectional resonant converter has a high conversion ratio in both power flow directions without using a transformer that has high turns-ratio. The operating principles and characteristics of the proposed bidirectional resonant converter are presented in detail. Experimental results using a 1-kW prototype verified the feasibility and efficiency of the proposed converter.

Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.