In this paper, the contemporary development in multiple input dc-dc converters are identified and examined. The quest to mitigate the difficulties associated with employing renewables in distribution systems and electric vehicles (EVs) has yielded many new converter topologies. These new topologies have easier control, lower parts count, are cheaper and are worthy alternatives to the typical series or parallel connection of converters. The converters are identified by three divisions that bother on the isolation between the respective ports. The electrically connected converters do not have isolation between the ports, and thus, a dc link connects the ports. Electromagnetically connected converters use a dc-link to connect input ports, but the input ports and output port are isolated. In magnetically connected converters, input ports are separated by multiple winding transformer, just as the output port is isolated from the input ports by the winding. The formation, structure, characteristics, operation, merits and demerits of the converters will be presented. Thereafter, comparisons will be done based on the distinct features of the converters. This review identifies that converter properties depend on the specific application requirement and thus, no converter fulfills all demands in the industry. Prospective future research trends are suggested. This work aims to update on research done during the time gap since the last comprehensive reviews.
In a photovoltaic (PV)-battery integrated system, the battery undergoes frequent charging and discharging cycles that reduces its operational life and affects its performance considerably. As such, an intelligent power control approach for a PV-battery standalone system is proposed in this paper to improve the reliability of the battery along its operational life. The proposed control strategy works in two regulatory modes: maximum power point tracking (MPPT) mode and battery management system (BMS) mode. The novel controller tracks and harvests the maximum available power from the solar cells under different atmospheric conditions via MPPT scheme. On the other hand, the state of charge (SOC) estimation technique is developed using backpropagation neural network (BPNN) algorithm under BMS mode to manage the operation of the battery storage during charging, discharging, and islanding approaches to prolong the battery lifetime. A case study is demonstrated to confirm the effectiveness of the proposed scheme which shows only 0.082% error for real-world applications. The study discloses that the projected BMS control strategy satisfies the battery-lifetime objective for off-grid PV-battery hybrid systems by avoiding the over-charging and deep-discharging disturbances significantly.
Developing countries need to make use of sufficient potential of PV power sources to cover the incremental demand of energy security. Though the PV-diesel microgrid system involving maximum supervising action as well as without having energy storage system can afford the continual power supply in the unelectrified rural area, it may not be circumstantially companionable because of the dependence on fossil-fuels and total dispatched energy cost [1][2]. Moreover, an individual PV system is an incomplete basis of electricity supplier due to the power instability produced by unpredictable solar irradiance and atmospheric temperature. Hence, MPPT is used commonly with PV solar systems to maximize power extraction from PV supply. Reference [3] presented an exhaustive literature review on on-line and off-line procedures for PV MPPT system. Reference [4] evaluated the application of Incremental Conductance, Perturb & Observe (P&O) MPPT procedure depending of European Efficiency Test EN 50530 that was specially contrived for Abstract: One of the major challenges for battery energy stowage system is to design a supervisory controller which can yield high energy concentration, reduced self-discharge rate and prolong the battery lifetime. A regulatory PV-Battery Management System (BMS) based State of Charge (SOC) estimation is presented in this paper that optimally addresses the issues. The proposed control algorithm estimates SOC by Backpropagation Neural Network (BPNN) scheme and utilizes the Maximum Power Point Tracking (MPPT) scheme of the solar panels to take decision for charging, discharging or islanding mode of the Lead-Acid battery bank. A case study (SOC estimation) is demonstrated as well to depict the efficiency (Error 0.082%) of the proposed model using real time data. The numerical simulation structured through real-time information concedes that the projected control mechanism is robust and accomplishes several objectives of integrated PV-BMS for instance avoiding overcharging and deep discharging manner under different solar radiations.
Though modern technology of new era is mostly dependent on power sector entirely, the current energy scenario is showing a serious negative effect for the last few decades. Comparatively, Bangladesh is facing a precarious effect because of the scarcity of fossil-fuel dissipation. To accomplish the power demand resolution, a new type of power generation is proposed in this research paper. Magnetic flux and solar irradiation is combined to get maximum power outcome. The PV panel supplies the maximum power in the peak solar radiation and terminates the energy stream at night time. However, the floating generator can supply its maximum creation day or night time according to the movement of water wave tendency. For this reason, a PV-floating Generator based integrated renewable energy scheme is inspected in this venture. The experimental result shows its real-world validation (Maximum 14.5 Watt output) comparing to conventional methods.
Power conveyance potentiality for series and parallel allied battery-packages are constrained by the wickedest cell of the string. Every cell contains marginally dissimilar capability and terminal voltage because of industrialized acceptances and functional situations. During charging or discharging progression, the charge status of the cell strings become imbalanced and incline to loss equalization. Therefore, the enthusiasm of this paper is to design an active charge balancing system for Lithium-ion battery pack with the help of online state of charge (SOC) estimation technique. A Battery Management System (BMS) is modeled by means of controlling the SOC of the cells to upsurge the efficacy of rechargeable batteries. The capacity of each cell is calculated by dint of SOC function estimated as a result of Backpropagation Neural Network (BPNN) algorithm through four switched DC/DC Buck-Boost converter. The simulation results confirm that the designed BMS can synchronize the cell equalization via curtailing the SOC estimation error (RMSE 1.20%) productively.
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