Development of dual functional converter for drive and charging power conversion for EV drive

The adaptability of electric vehicle drives is primarily concerned with the size and efficiency of power conversion. This paper presents a unified power converter for the drive and charge functions of brushless direct current-based electric vehicle drives (BLDC). The symmetrical utilization of BLDC phase windings during charging operation is implemented for efficient power conversion. The unified converter operation, configuration, and control are presented. The proposed converter is simulated in the MATLAB/Simulink platform. The performance is evaluated using operational variables such as voltage, current, torque, and speed. A comparative study is presented regarding the size and efficiency of the proposed and existing drives. The proposed drive achieved 0.01 p.u. ripple in torque, 10-sec transient time for a change in speed full throttle command, and unity power factor current for charging operation, proving its robustness over the comparable drives

Parametric analysis on the effect of V-type rotor magnet geometry on the dynamic performance of PMSMs

The research examines how different dimensions of V-type permanent magnet synchronous motor (PMSM) magnets influence the magnetic flux between the rotor and the stator system because matching these dimensions optimizes the magnetic flux for better torque production. As long as the magnet size stays within the right dimensions, it builds greater flux density, which leads to better torque output and better efficiency. Research confirms that flow barriers strengthen engine capabilities. The research applies parametric optimization to find the perfect magnet shapes while showing how they boost electric vehicle motors to meet their requirements. Our tests with finite element method (FEM) show how changing magnet dimensions affects performance. Researchers adjust magnetic measurements frequently until the optimal setup of 50 mm thick by 4.5 mm wide emerges. Their action boosts flux density, which improves motor torque and energy capacity. At these optimal dimensions, the engine achieved 95% efficiency with precise flow barrier adjustments that helped increase torque output while reducing unstable electricity output

Internet of things (IoT) based monitoring system for hybrid powered E-bike charging station

The internet of things (IoT) has become an important foundation in the development of web-based and remote technologies. In the implementation of renewable energy in hybrid E-bike systems, IoT-based monitoring system integration has made a significant contribution to monitoring activities. One of the latest innovations in the development of IoT in E-bike systems is the application of power prediction and the Coulomb counting method to estimate the charging time for a battery with a capacity of 200 AH, so that users can know the time needed to charge the battery efficiently. The IoT E-bike system is designed with user data display and monitoring features via the website, such as data on voltage, current, light intensity, battery percentage, power prediction, and prediction of the resulting battery charging time. Experimental results were obtained during the battery charging period, increasing the battery percentage from 50.43% (10 volts) to 71.769% (11.3 volts) in 4.5 hours with a battery charging charge of 153,866.4 C

Simulation and verification of improved particle swarm optimization for maximum power point tracking in photovoltaic systems under dynamic environmental conditions

This paper introduces an improved particle swarm optimization (iPSO) algorithm designed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed algorithm incorporates a novel reinitialization mechanism that dynamically detects and adapts to environmental changes. Additionally, an exponentially decreasing inertia weight is utilized to balance exploration and exploitation, ensuring rapid convergence to the global maximum power point (GMPP). A deterministic initialization strategy is employed to uniformly distribute particles across the search space, thereby increasing the likelihood of identifying the GMPP. The iPSO algorithm is thoroughly evaluated using a MATLAB/Simulink simulation and validated with real-time hardware, including a boost DC-DC converter, dSPACE, and a Chroma PV simulator. Comparative analysis with conventional PSO and PSO-reinit algorithms under various irradiance patterns demonstrates that the iPSO consistently outperforms in terms of convergence speed and MPPT efficiency. The study highlights the robustness of the iPSO algorithm in bridging theoretical models with practical applications

Artificial raindrop algorithm for control of frequency in a networked power system

Load frequency control (LFC) evaluates the net changes in generation by continuously monitoring tie-line flows and system frequency required relying on load changes. It adjusts generator set points to minimize the area control error's (ACE) time-averaged value. ACE is regarded as a controlled output of LFC. Previous research focused on customary power systems like hydro-hydro, thermal-thermal, and hydro-thermal configurations. This current development study introduces the hybrid PV and dual thermal system interconnected systems for LFC analysis. The research evaluates LFC performance with different controllers, considering parameters such as maximum peak overshoot (Mp), maximum undershoot (Mu), settling time (Ts), and peak time (Tp). Controllers, including proportional integral (PI), anti-windup PI, fuzzy gain scheduling PI, and A cutting-edge algorithm generating fake raindrops are used for minimize ACE. The analysis introduces various load perturbations to observe controller performance in interconnected power systems. Both PV-thermal-thermal and thermal-thermal-thermal systems exemplify innovative approaches to energy management that bolster energy efficiency and sustainability. By integrating these advanced systems, we can make significant strides towards achieving global sustainability goals and promoting a cleaner and support energy efficiency for the future

Comparison of speed loop control methods for IPM motor in electric vehicles

With its outstanding features, such as high efficiency and torque-producing capability compared with the induction motor, the interior permanent magnet synchronous motor (IPMSM) has been increasingly researched and used for electric vehicles. The speed control strategy for both low and high speeds of the IPMSM is studied in conjunction with controllers based on the field oriented control (FOC) structure to ensure accurate and stable system response under various operating conditions. This paper focuses on three control methods: sliding mode control (SMC), backstepping (BSP), and proportional integral (PI) for the speed loop to enhance system stability. Coupled with the presence of load disturbances, environmental disturbances, and uncertainties in parameters, comparisons and observations regarding the three methods can be made to conclude system stability and performance. Finally, simulation results on MATLAB/Simulink software confirm the effectiveness and validity of the proposed speed controllers

LQG-based optimal control approach of an electronic throttle valves using DC servo system

A direct current (DC) motor is used for automotive electronic throttle valves (AETV) to adjust incoming air into the engine’s combustion system, which has many advantages such as smooth, fast response, and simplicity. However, high-accuracy tracking control for AETV faces various obstacles because of the nonlinear features, hard identification, and noise. In this paper, a model of the AETV with four states in the form of a state space is developed. Then, a Kalman filter is formulated to eliminate the impact of measurement noise. The Kalman filter gain is obtained via the solve the linear quadratic gaussian (LQG) equation. Next, the optimal control based linear quadratic regulation (LQR) and Kalman filter are presented in which the control gain is constructed by the Riccati equation with the assistance of MATLAB/Simulink software. Finally, simulation studies are conducted to demonstrate the efficiency of the suggested method for the AETV system with other control strategies

Dual-aware EV charging scheduling with traffic integration

Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a

Digital twin-based performance evaluation of a photovoltaic system: A real-time monitoring and optimization framework

The digital twin (DT) technology implementation in photovoltaic (PV) systems provides an innovative approach to real-time performance monitoring and predictive maintenance. In this paper, an end-to-end DT framework for real-time performance analysis, fault detection, and optimization of a 250 W PV system is proposed. A physics-based equation and AI-based prediction hybrid DT model is developed through MATLAB/Simulink, trained from real data acquired by means of a testbed. The DT simulates the dynamic physical PV system behavior and adjusts itself using self-correcting algorithms to enhance precision in prediction and forecast power output at high fidelity. Results indicate that the DT gives the true response of the PV system with very small differences attributable to model approximations and sensor faults, 95% error minimization after compensation, and a root mean square error (RMSE) of 2.8 W, indicating its applicability for real-time monitoring and predictive main-maintenance. The work here focuses on the feasibility of applying DTs towards the autonomous optimization of distributed renewable energy systems

Fuzzy logic-based energy management system for a microgrid with hybrid energy storage: design, control, and comparative analysis

This paper presents a fuzzy logic-based energy management controller for a microgrid with a hybrid energy storage system. The microgrid integrates intermittent renewable energy sources. To provide high quality, reliable and sustainable power, the microgrid depends on energy storage devices. The proposed fuzzy logic-based energy management controller controls the energy storage system’s power electronic converters by generating switching pulses based on the generation availability, load requirement, SOCs of battery, and supercapacitor. Additionally, a fuzzy logic-based energy management system is planned in such a way that high power needs are satisfied by supercapacitors and high energy needs are satisfied by batteries. To highlight the key benefits of utilizing a fuzzy logic-controlled hybrid energy storage system over PI -a controller-based cascaded dual loop energy management system, a comparative study is carried out. The results of the same is discussed elaborately in this paper. These studies were simulated using the MATLAB/Simulink software package