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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]

[Display omitted] • The kinetics of lithium ions during de/lithiation was studied by analyzing the diffusion coefficient. • The overpotential through GITT was measured at different temperatures and current rates. • The charge transfer resistance was evaluated using EIS at different temperatures and voltages. • The kinetics of lithium diffusion is most feasible at an optimum condition of the current rate and operational temperature. • The fluctuation in the diffusion coefficient values is associated with the structural changes in NCA. Cathode degradation during the charge and discharge process is inevitable due to phase transformations during the de-/lithiation process. Various factors such as temperature, cut-off voltage, and current rates tend to cause irreversibility in the phase transformations during cycling, resulting in the quick aging of lithium-ion batteries for electric vehicle applications. This study aims to evaluate the trends of the lithium-ion diffusion coefficient as a function of the lithium-ion concentration and potential in the lithium nickel cobalt aluminum oxide (NCA) cathode of the VTC5 lithium-ion cell through measurements by the galvanostatic intermittent titration technique (GITT). The lithium-ion cell has shown the lowest diffusion coefficient value of 1.08E−17 m2 s−1 at 0.2C rate at 25 °C temperature and the highest diffusion coefficient of 7.2829E−16 m2 s−1 at the lowest current rate of 0.05C rate and 45 °C temperature during discharging. The trend of diffusion coefficient values for lithium de-/intercalation with changing temperature and current rates is investigated and correlated with the phase transformations in the structure of the cathode and is indicative of a mixed-phase reaction mechanism. The electrochemical impedance spectroscopy data reinforces the dependence on lithium-ion diffusion on temperature, and voltage values and is supported by comparative analysis. [ABSTRACT FROM AUTHOR]

• A novel control scheme for a PUC inverter has been proposed for the BESS application. • The proposed scheme uses a finite-set MPC with an inner and outer loop. • Good dynamic performance and fault-ride-through capability have been achieved. • Grid code compliance at low switching frequency has been demonstrated. Compared to conventional multilevel topologies, the Packed U-Cell multilevel topology utilizes the minimum number of components and provides both maximum voltage levels and high reliability. When used in a battery energy storage system, its dc-link consists of a top branch battery storage and bottom branch flying capacitors, whose state of charge and voltages must be balanced.This paper proposes a novel control scheme with two cascaded controllers: the inner loop regulates the output filter current and dc-link voltages using a novel finite-set model predictive control, the outer one is fully integrated with the model predictive controller and regulates the battery state of charge by injecting a zero-sequence voltage. The proposed control scheme was successfully implemented and tested through simulation and real-time testing. The obtained results highlight good dynamic performance and robustness of the proposed scheme during active power injection/absorption and low voltage ride-through capability. [ABSTRACT FROM AUTHOR]

With the advent of SiC devices, the packed U-Cell converter has become a promising topology among the multilevel converters for medium voltage DSTATCOM application. It utilizes fewer components while achieving the maximum voltage levels with higher reliability. However, as a reactive power compensator, its dc-link consists of flying capacitors, whose voltages must be balanced which presents a challenging problem for a seven-level packed U-Cell inverter, as there are no-redundant switching states. This paper proposes a novel control scheme to keep these flying capacitors balanced under normal/abnormal grid conditions. It consists of a standard cascaded: inner and outer controllers. The inner current controller regulates both the active and reactive currents and the outer controller regulates the upper flying capacitor voltage. In parallel with the cascaded controllers, a novel model predictive voltage balancing controller is proposed to maintain the lower flying capacitor voltage at a given setpoint. To cope with the unbalance grid conditions, an additional zero-sequence injection regulator is also integrated into the designed control scheme. The proposed control scheme was simulated and real-time tested on the Hardware-In-the-Loop system. The results show that the proposed controller effectively balances the two flying capacitor voltages while controlling the reactive power. [ABSTRACT FROM AUTHOR]

Photovoltaic distributed generation (PVDG) has seen tremendous growth in recent years, especially in the residential sector. Among other concerns, the voltage rise in AC networks is considered the most limiting factor in achieving increased PV penetration levels. A steady-state impact study is performed on a CIGRE low-voltage (LV) residential network. This paper compares six techniques to increase the PV penetration limit in the LV residential network, namely single-phase penetration (SPP), Distribution Scheme 1 (DS1), Distribution Scheme 2 (DS2), alternate phase penetration (APP), offline tap adjustment (OTA) and switched on-load tap adjustment (SOLTA). PSCAD software is used for this study. The best results are obtained for the DS2-SOLTA case that gives the minimum voltage magnitude and voltage unbalance in the system. The steady-state results are validated by a dynamic data study using measured solar irradiance and residential load data. A novel approach is also proposed for calculating the worst day from the data set. The obtained results verify the effectiveness of the proposed approach. [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]

Researchers are focused toward the development of electric vehicles and hybrid electric vehicles for the preservation of the ecosystem. Energy storage, conversely, became essential with these changes in vehicular technology. This opens new research fronts and introduces complex problems that are to be investigated regarding the primary storage element. Battery modeling offers improved efficiency, safety, and reliability. Lithium-ion batteries are a popular source of energy in many applications, especially for electric vehicles because of their high performance and high energy density. However, there exist some inherent issues like complex electrochemical reactions that make accurate monitoring a challenging task. This article performs a comparative study of the existing battery state of health (SOH) monitoring and capacity estimation techniques using the NASA dataset for lithium-ion batteries. Charging cycle datasets for voltage, current, and temperature are used for feature identification and extraction. After the modeling of extracted features, the SOH and the capacity of four batteries are estimated using three data-driven techniques. Including convolutional neural network, feed-forward neural network, and long short-term memory (LSTM), and their results are compared. The comparative analysis highlights an outstanding performance and high accuracy of the LSTM-based machine learning technique because of the inherited long-term memory of the LSTM. The study, therefore, recommends the use of LSTM to researchers for battery health monitoring and capacity estimation with the highest possible accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a five level cascaded converter for Distribution Static Compensator (DSTATCOM) implementing a novel modulation algorithm with harmonic mitigation and a PI controller with phase shift modulation for reactive power control. The modulator operates at fundamental frequency and the whole system satisfies grid code requirements using a simple passive output filter tuned with 11th harmonic. The behavior of the proposed system has been successfully compared with another one with multilevel space vector pulse width modulation. Results show its capability of ensuring satisfactory dynamic response at low frequency operations, resulting improved efficiency and reduced output filter requirements over more conventional systems. [ABSTRACT FROM AUTHOR]

The power sector of Pakistan has undergone a substantial, yet protracted power sector reform program in 1992. Under this program, the government allowed participation of Independent Power Producers (IPPs), followed by the breakup of the Water and Power Development Authority (WAPDA) into various entities and the establishment of power sector regulatory authority, namely NEPRA in 1997. The power sector reforms in Pakistan have not been evaluated sufficiently to determine the actual costs and benefits incurred by different segments of the society, to correct the path of upcoming reforms. This study attempts to investigate the socio-economic impacts of restructuring of WAPDA's generation segment and private sector participation in the electricity generation sector of Pakistan by using a social cost-benefit analysis approach. The study has analyzed the impacts of reforms on government, private sector, and consumers (domestic, commercial, industrial and agriculture) of the power sector. The results have shown that the main benefit of private sector participation came from the timely expansion of generation capacity. However, environmental and electricity generation costs became exorbitant after power sector reforms. Consumers and government remained net losers, while IPPs were winners in the process of restructuring and privatization. This paper concludes that power generation sector reforms with private sector participation have impacted the overall social welfare adversely. It further concludes that had regulation been scrupulous with adequate supportive role of government with social obligations, consumers and government could have benefited from restructuring and privatization of the power sector generation sector in Pakistan. • Power sector of Pakistan is going through reforms. • Reforms need to evaluate its costs and benefits. • Social cost-benefit analysis utilized. • Private sector gained from the reforms and social costs were higher. • Need for a better structure for the future reform. [ABSTRACT FROM AUTHOR]

Regarding different challenges, such as integration of green energy and autonomy of microgrid (MG) in the multi-microgrid (MMG) system, this paper presents an optimized and coordinated strategy for energy management of MMG systems that consider multiple scenarios of MGs. The proposed strategy operates at two optimization levels: local and global. At an MG level, each energy management system satisfies its local demand by utilizing all available resources via local optimization, and only sends surplus/deficit energy data signals to MMG level, which enhances customer privacy. Thereafter, at an MMG level, a central energy management system performs global optimization and selects optimized options from the available resources, which include charging/discharging energy to/from the community battery energy storage system, selling/buying power to/from other MGs, and trading with the grid. Two types of loads are considered in this model: sensitive and non-sensitive. The algorithm tries to make the system reliable by avoiding utmost load curtailment and prefers to shed non-sensitive loads over sensitive loads in the case of load shedding. To verify the robustness of the proposed scheme, several test cases are generated by Monte Carlo Simulations and simulated on the IEEE 33-bus distribution system. The results show the effectiveness of the proposed model. [ABSTRACT FROM AUTHOR]

Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures. [ABSTRACT FROM AUTHOR]