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Switched reluctance motors (SRMs) face challenges in achieving high performances in terms of the power factor, torque per ampere, and torque ripples. Although several parameters impact their performance, magnetic saliency has a significant impact. This paper presents a study on the influence of the saliency ratio on the performance of switched reluctance motors. A detailed design procedure for a mid-drive electric motor for a high-performance electric bike is presented. A systematic methodology is adopted to estimate the power rating for desired vehicle performance while considering the vehicle's mechanical and aerodynamic parameters. Additionally, the paper discusses and analyzes the design of two SRM motor configurations with different rotor poles (6/4 and 6/10) while performing magnetostatic analysis for generated torque validation. A parametric study is performed on the 6/10 SRM design for further improvement in the torque profile while keeping the same fill factor and electrical loading. Furthermore, the transient analysis in Ansys Maxwell 2D for both SRMs confirms the designed performance. The results shows that the SRM with higher rotor poles (6/10) is a better choice. [ABSTRACT FROM AUTHOR]

Demand response (DR) strategies are recieving much attention recently for their applications in the residential sector. Electric vehicles (EVs), which are considered to be a fairly new consumer load in the power sector, have opened up new opportunities by providing the active utilization of EVs as a storage unit. Considering their storage capacities, they can be used in vehicle-to-grid (V2G) or vehicle-to-community (V2C) options instead of taking power in peak times from the grid itself. This paper suggests a community-based home energy management system for microgrids to achieve flatter power demand and peak demand shaving using particle swarm optimization (PSO) and user-defined constraints. A dynamic clustered load scheduling scheme is proposed, including a method for managing peak shaving using rules specifically designed for PV systems that are grid-connected alongside battery energy storage systems and electric vehicles. The technique being proposed involves determining the limits of feed-in and demand dynamically, using estimated load demands and profiles of PV power for the following day. Additionally, an optimal rule-based management technique is presented for the peak shaving of utility grid power that sets the charge/discharge schedules of the battery and EV one day ahead. Utilizing the PSO algorithm, the optimal inputs for implementing the rule-based peak shaving management strategy are calculated, resulting in an average improvement of about 7% in percentage peak shaving (PPS) when tested using MATLAB for numerous case studies. [ABSTRACT FROM AUTHOR]