Summary & Conclusion
Open-loop operation (design 1 and 6) resulted in poor PF and high THD values. CICM converters were employed for PFC, namely isolated SEPIC and Boost. Among all, it was noticed that isolated SEPIC DC-DC converter (designs 3 and 5) performs better than Boost (designs 2 and 4), and this is because isolated SEPIC has an inherent PFC feature where it acts as a resistive load from the grid’s perspective. Following the predefined constraints and standards presented in Chapter 1, the simulation was conducted by supplying different designs with 240Vrms 50Hz for single-phase systems and 415VLLrms 50 Hz, and the values are shown in Figure 4-4 to show a comparison between the different designs and their compliance with the set constraints. The designed systems do not require isolation between the grid and the converter because the maximum voltage in the system –with respect to ground– did not reach 1kV.
Phase-modular active PFC systems generally employ three single-phase PFC circuits, and the main advantage of them lies in the modularity, redundancy, and utilizing existing improved-single-phase active PFC systems. Also, isolation in phase-modular systems is vital because an output power of 50kW requires a parallel output connection. Off-board chargers have to be supplied from a three-phase power supply to satisfy the high power requirement. Based on that, results have shown that phase-modular active PFC generally performs highly compared to direct three-phase active PFC.
Recommendations for Future Work
In this phase of the project, different systems were designed and simulated. The optimal design has not been fully practically implemented due to time constraints. A contingency plan was proposed and followed. Further research on isolated SEPIC converter can be conducted as a future work. The future work also includes the practical implementation of the final system following the design parameters and constraints. Different charging mothods tha are suitable for Li-ion Batteries can be considered. Moreover, enhancements on the system intelligence by designing an AI-based controller for battery condition monitoring through collecting data for both charging and discharging processes. Machine learning algorithms cab be used to train the system model for condition monitoring purposes. The mobile app can be further developed to include more services and options as well.
Go Back to FG4 Main Page