In this paper, a novel soft-switching-technique for quasi-single-stage$(\text{QS}^{2})$bidirectional DC-AC dual-active-bridge (DAB) converter is presented. The soft-switching-technique results in the triangular transformer current due to unsymmetrical switching of DC side H-bridge. DC side H-bridge of$\text{QS}^2$DAB consists of two branches, one branch supplied with fixed ON time pulse width modulation (PWM) signal. However, a variable ON-time controlled PWM is provided to another branch of the H-bridge. Due to this nature of switching, unsymmetrical H-bridge triangular modulation (TRM) is referred. The proposed modulation offered several advantages such as 1) near unit power factor correction (PFC) operation for bidirectional flow. 2) zero current switch (ZCS) turn ON and OFF for pulsating DC side H-bridge. 3) zero voltage switch (ZVS) turn ON and ZCS turn OFF for the switches of branch 2. 4) ZVS/ZCS turn ON for the switches of branch 1. 5) true soft-switching over the whole envelop of the line frequency. 6) improved performance with reduced total harmonics distortion (THD). A scale-down experimental prototype of 580 W is developed and the proposed technique is realized using TMS320F28335. A good power factor of 99.98% and THD of less than$< 2.71\%$with peak experimental efficiency of 95.7% achieved using the proposed modulation technique.

In this paper, the development of an efficient three- layer Cyber Physical System (CPS) using a multi-output power electronic interface integrated with IoT-enabled modules and AI- based algorithms is presented. The power electronic interface layer of the proposed CPS eliminates the need of bulky electrolytic capacitors and reduces the switching loss. This layer facilitates DC to DC and DC to three-phase AC power conversion for battery charging and smart-grid applications respectively. AI-based algorithms are utilized for accurately predicting the Remaining Useful Life (RUL) of batteries. The performance of the proposed CPS is validated through simulation and experimental measurement results using a 700 W prototype system. The measurement results show that the proposed CPS enables real-time monitoring, accurate prediction of RUL, predictive maintenance decisions, data analytics, and optimized power flow.

Many modern appliances including Electric Vehicles use two batteries at two different voltages. Charging one battery from the other requires bidirectional DC-DC converter that charges the battery with lower state of charge. In addition to the converter, a charging algorithm is required for the optimal charge of the battery. There are three-existing optimal battery charging techniques; 1) constant current-constant voltage (CC-CV) charging, 2) pulse charging, and 3) reflex charging. However, these charging techniques require different control circuits for their implementation. Moreover, in the conventional chargers, the changeover from CC to CV is unsmooth as the circuit does not remain in the feedback control during the transition. To address the above issues, a fourth-order minimum-phase bidirectional DC-DC converter (BDC) with an adaptable feedback clamped modulator is proposed in this paper. The proposed charger is capable of adapting to all the three-existing charging techniques by controlling the clamping voltage. The steady-state behavior of the BDC is verified through a 250 W prototype. Also, the performance of the charger using CC-CV charging is experimentally verified using a 6 V- 4.5 Ah lead-acid battery.