Department of Electrical and Electronics Engineering

Accurate estimation of battery parameters such as resistance, capacitance, and open-circuit voltage (OCV) is absolutely crucial for optimizing the performance of lithium-ion batteries and ensuring their safe, reliable operation across numerous applications, ranging from portable electronics to electric vehicles. Here, we present a novel approach for estimating parameters that combine the two RC equivalent models with the variational and logistic map cuckoo search (VLCS) algorithm. To accurately estimate the parameters of a battery, an experimental setup is designed to carry out a range of tests under controlled laboratory operating conditions. These tests include the Hybrid Pulse Power Characterization (HPPC), OCV, and capacity tests. The OCV test helps to establish the relationship between the state of charge and the OCV, while the HPPC test provides a variable schedule of ‘C’-rates, which allows for a better understanding of the battery’s behavior under different load conditions. The result of the experiment shows that the proposed establishment is effective to accurately determining parameters under different C-rates. After performing a comparative analysis, it is found that the VLCS algorithm outperforms in contrast to standard algorithms such as genetic algorithm, particle swarm optimization, and cuckoo search algorithm. The algorithm mitigates voltage variation between experimental and simulation results, resulting in an approximate error percentage of 0.23%. The root mean square error is employed as a performance indicator, which demonstrates the superiority of the proposed approach. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

The state of charge (SoC) of a battery is a crucial monitoring indicator for battery management systems and it helps to assess how much further an electric vehicle can travel. This work proposes a novel approach for predicting battery SoC by developing a closed-loop system that integrates a bidirectional long short-term memory neural network with an innovative algorithm-extended Kalman filter. A second-order equivalent circuit model is selected, and its parameters are computed using the variational and logistic map cuckoo search approach. Further, an Extended Kalman filter is combined with an innovation algorithm to update process noise in real-time, and a bidirectional long short-term memory neural network takes the input from the Extended Kalman filter and gives the compensated error value for the final SoC estimation.

In low and medium voltage distribution networks, the LCL integrated conventional converter based distributed static compensator (D-Statcom) has shown to be a practical solution for shunt compensation. Despite numerous efforts in this area, the traditional control approach still has a number of issues. This article describes the development of LCL integrated D-Statcom for shunt compensation utilizing a deep learning technique. The voltage source converter (VSC) and LCL filter are included in the new framework. The operation and control of the distribution network are directly impacted by the proposed system's performance. MATLAB Simulink software and an experimental research based on d-SPACE are used to demonstrate the synchronization, which solves current related power quality (PQ) issues such as poor power factor (p.f.), current harmonics, unbalanced voltage at point of common coupling (PCC) and poor voltage regulation. In order to provide precise reference currents for control, the deep learning technique is used to monitor the essential active and reactive components of load currents. Furthermore, it precisely ascertains the remaining constituents, attaining a swifter transient reaction and superior system stability. Comparisons are then made between VSC and LCL integrated VSC using deep learning technique by considering the implementation procedure. With a lower DC-link voltage and a smaller converter power rating, the LCL integrated VSC system improves PQ of distribution network more than the VSC.

Lithium-ion batteries are an intuitive choice for electric vehicles and many other gadgets. Parameters play a critical role in addressing its performance characterization. Accurate parameter estimation and real-time monitoring of lithium-ion batteries are important in modeling equivalent circuits. The characteristics of lithium-ion batteries are dynamic due to energy storage. Dynamical behavior is characterized by RC equivalent models. This work presents the estimation of parameters associated with the n-RC equivalent circuit model in integration with the Improved Cuckoo Search Algorithm (ICSA). To get it, battery tests such as HPPC test, static capacity test, and open circuit voltage test in consideration of temperatures are carried out. The experiments are carried out under different temperature ranges to record the valid data sets. ICSA is advantageous over existing algorithms in estimating the battery parameters under temperature ranges. The performance of the proposed approach captures and estimates the parameters in the dynamic range of temperatures of the lithium-ion battery. The error profile is addressed with the root mean square error and it is found to be 0.23 % at 30 °C. It is observed that experimental data with ICSA accurately matches the simulated model data at different temperature ranges

A quadral-duty digital pulse width modulation (QDPWM) control-based hardware accelerator for the auxiliary permanent magnet brushed DC (PMDC) motors of electric three-wheelers (E3Ws) is proposed. The proposed accelerator involves a precise motor speed calculation circuit, including a buffer to hold the position encoder signal for a predefined number of clock cycles to eliminate encoder signal noise. The proposed hardware accelerator is described with supporting mathematical models and is implemented on field-programmable gate array (FPGA) as well as application-specific integrated circuit (ASIC) platforms using SCL 180 nm CMOS technology library. The ASIC implementation at 12.5 MHz shows that the proposed design has significantly less area and power consumption than the conventional PI-PWM controller-based architecture and is comparable to the dual-duty digital pulse width modulation (DDPWM) controller. The proposed FPGA prototype-driven motor attains a wider speed range with low-speed ripple than DDPWM controller-based architecture. The position signal buffer circuit also enables the accelerator to tolerate noise or glitches in the position encoder signal, which makes the speed calculation precise and reliable. The proposed hardware accelerator-based PMDC drive performance has been validated regarding settling time, speed tracking ability, tolerance to dynamic speed, and load variations on a laboratory test setup

The rising demand for sustainable transportation has sparked significant interest in solar-powered electric vehicles (EVs). However, integrating solar energy into EV drivetrains, particularly those using Permanent Magnet Synchronous Motors (PMSMs), presents challenges due to the occasional nature of solar power needed for consistent vehicle performance under varying environmental conditions. This paper introduces a high-performance solar-fed PMSM system for electric vehicles, incorporating advanced control techniques and an intelligent energy management strategy (EMS). The system employs Field-Oriented Control (FOC) for precise motor speed regulation and a Fuzzy Logic-based Maximum Power Point Tracking (MPPT) algorithm to optimize solar energy harvesting. A lithium-ion battery serves for efficient energy storage, enabling the system to store and use solar power effectively. The EMS dynamically allocates energy between the solar panels, battery, and motor, maximizing energy efficiency and extending the vehicle's range. The system was tested in MATLAB/Simulink simulations and validated using dSPACE DS1104 hardware for real-time control. The simulation results, coupled with hardware testing, demonstrate improved energy efficiency and reduced reliance on external charging sources. These findings position solar-powered EVs as a competitive and sustainable solution for the future, offering significant benefits to industries in EV manufacturing and renewable energy. The integration of solar power not only enhances sustainability but also addresses the growing demand for green and efficient transportation

Green energy and clean power are the recent trends of modern power distribution net-works (PDN). In recent years, great attention has been focused on T type inverters due to their advantages over conventional voltage source inverters (VSI), such as fault-tolerant,overload capability, less total harmonic distortion (THD), better output waveform and high efficiency. An inductor coupled T type (IC-T type) inverter-based distribution static com-pensator (DSTATCOM) is built for active power filtering of 3-phase 3-wire PDN connected nonlinear load in this paper. The proposed topology is composed of three inductors connected between the VSI and common source. The proposed PDN is obstructed by the DSTATCOMusing icos control algorithm for the inverter DC link voltage reduction, filter inductor rating minimization, decreasing the switching stress, increasing the life span of an inverter, reliable operation, stress balancing, loss reduction and increase in efficiency. Apart from these, other improvements such as power factor (PF) correction, better voltage regulation, harmonics re-duction and load balancing are obtained. The efficacy of the IC-T type inverter in different loading scenarios is justified using MATLAB/Simulink software captivating in reflection ofthe IEEE-514-2017 and IEC- 61000-1-3 benchmark

Accurate prediction of electricity consumption is essential for the efficient functioning of power grids and the successful administration of energy markets in the field of energy planning. There are several benefits such as system efficiency, dependability, safety, and stability with the proper forecasting of load demand. To ensure the proper operation of the microgrid energy management system, it is essential to predict the overall load demand with precision and consistency. A bidirectional long short-term memory network (Bi-LSTM) model is considered in this paper for the electrical load forecasting of microgrids. The proposed model is verified by using the standard available load forecasting data set of Panama City. Furthermore, the approach is compared with LSTM load forecasting method. The various performance metrics such as MAE, RAE, RSE, R2, RMSE, and NRMSE are employed in this paper to evaluate the accuracy of the proposed prediction model.. In this work, the Bi-LSTM method got the better error reduction values of 17.8% in MAE, 1.70% in RMSE, 2.37% in NRMSE, 3.5% in RSE, 19% in RAE and 1.2% improvement in R2 when compared to the LSTM model. The proposed load prediction model is helpful in estimating the future power shortages of the microgrid community which leads to improve the system efficiency and reliability

This paper introduces a new leg-integrated switched capacitor inverter (LISCI) structure for efficient three-phase induction motor operations powered by solar panels. Traditional inverter configurations often face challenges related to efficiency, size, and cost. The presented LISCI structure addresses these issues by integrating switched capacitor networks directly within the inverter legs, offering significant improvements in performance and compactness. Key features of the LISCI structure include reduced component count, enhanced voltage gain, and improved harmonic performance. The inverter’s innovative design enables it to achieve higher efficiency by minimizing switching losses and optimizing power distribution. Additionally, the integrated capacitors contribute to a more stable voltage output, critical for the reliable operation of three-phase induction motors

Power and frequency instability pose significant challenges in microgrid operation, which restricts load sharing and degrades dynamic performance. Existing control methods often involve trade-offs between stability and power sharing. Conventional power system stabilizers (PSS) utilize lead-lag compensators with parameters selected arbitrarily, resulting in less than optimal performance during disturbances. This research paper presents a novel, generalized PSS designed for inverter-based microgrids. It incorporates an adaptive Honey Bee Optimization (HBO) algorithm for dynamic tuning of the lead compensator parameters T1,T2, and gain K. Unlike traditional methods, the proposed HBO-PSS improves the damping of low-frequency oscillations and enhances power sharing accuracy, while maintaining stable output voltage. The time-domain simulation results indicate that the adaptive HBO-PSS demonstrates superior performance compares to existing methodologies. The proposed PSS facilitates faster and more equitable power sharing, while also enhancing stability significantly, even in the presence of switching disturbances and higher droop coefficients. This work simplifies the implementation and analysis of PSS while facilitating future research into decentralized control strategies for distributed energy systems

A new customized multi-level inverter (MLI) configuration is proposed for induction motor drive, aiming to lower the requirement of DC bus voltage magnitude. This method utilizes pole pair winding coils separately to generate multi-level voltage waveform across the total stator phase windings. As the inverter requires lower input voltage it eliminates the requirement of boost converters when it is used in the EV applications. The inherent advantages of this topology significantly reduce control complexity in the battery systems by reducing the number of series-connected battery cells. The conventional LevelShifted Sine Triangle PWM technique proficiently shifts low-frequency harmonics to the carrier frequency, enhancing power quality and minimizing electromagnetic interference. Through MATLAB simulation, this new customized multi-level inverterfed open-end stator winding Induction motor is simulated and results are presented to validate the proposed concept. Ultimately, our research aims to contribute to advancing electric vehicle technology by operating the induction motor with minimal input DC source voltage, and substantial output gain

This study examines the integration of permanent magnet synchronous motors (PMSM) with renewable energy sources, focusing on solar photovoltaic (SPV) arrays to improve efficiency and sustainability in electric vehicle (EV) applications. PMSM, renowned for its high efficiency, silent operation, and precise control, is managed using a proportional-integral (PI) controller to handle variable load conditions, including fluctuations in torque and current. By fine-tuning the PI controller’s gains, the desired motor speed is achieved efficiently. A DC-DC Buck-Boost converter serves as an intermediary power conditioning unit, optimizing energy extraction from the SPV array and enhancing system efficiency. This setup ensures that PMSM meets the power and operational demands of EVs. Additionally, a voltage source inverter (VSI) facilitates electronic commutation of the PMSM, providing accurate control using fundamental frequency pulses. The system is modelled and simulated in MATLAB/Simulink, demonstrating its reliability under diverse load conditions. The findings underscore the potential of this approach in promoting renewable energy integration in EVs, paving the way for cleaner and more sustainable transportation solutions

Due to environmental concerns, renewable energy has gained significant popularity over the past two decades. Integrating distributed generation and renewable energy sources, particularly through microgrids in power distribution systems, has become feasible. Additionally, there has been a notable increase in the adoption of electric vehicles (EVs) driven by environmental initiatives and their advantages over internal combustion engines. As a result, the planning and operation of microgrids in distribution systems have become more complex. To address these complexities, computational evolutionary algorithms have emerged as effective solutions. The Differential Evolution (DE) algorithm stands out for its speed and userfriendly simplicity. The proposed study uses the Fuzzy Adaptive Differential Evolution (FADE) analysis for microgrid planning integrated with EV charging infrastructure, using the IEEE 33bus system. The FADE algorithm combines the power of fuzzy logic and adaptive strategies within the DE framework to tackle the planning and optimization challenges of microgrids integrated with Electric Vehicle Charging Stations (EVCS). The findings provide valuable insights into the effectiveness of the FADE algorithm in addressing the challenges associated with the planning and operation of microgrids with EVCS in modern power systems

Microgrids have gained more attention in recent days due to the efficient integration of various distributed energy resources. However, the load power sharing between the distribution generators (DGs) in the microgrids is one of the major challenges, especially at the peak load demand condition. This paper proposes a finite control set-based model predictive controller (FCS-MPC) for the DG-fed inverters in microgrid applications. A universal droop controller model is also considered into account to generate the reference values for the proposed FCS-MPC controller for improved power sharing. The main goal of this paper is to efficiently regulate the power flow from/to the parallel DGs in a microgrid environment. The proposed control method is able to share equal load power, though there is a mismatch in line impedances in the AC microgrid network. In this study, the microgrid test system with two parallel DGs is used to evaluate the performance of the proposed model. To show the effectiveness of the proposed control method, the simulation results of the proposed model have also been compared with the conventional droop control technique. The proposed model has superior performance compared to the conventional droop controller in terms of load power sharing and maintaining tolerance limits, as evidenced by the simulation results

A boost converter is a type of device used to convert direct current (DC) from one voltage level to another. It operates by increasing the input voltage to a higher output voltage level.DC-DC converters are utilised in a wide range of applications. The primary application of boost converters is to establish an interface with renewable energy sources.This paper presents a comparative analysis of two controllers, namely the Proportional Integral controller and Model Predictive Control (MPC), for practical applications of the Boost converter. The boost converter is designed and simulated in the MATLAB/SIMULINK environment for this study.The performance of the MPC controller is found to be superior when compared to the PI controller.The effectiveness of the proposed control scheme is validated through the utilisation of OPAL-RT

This paper presents an optimized control strategy for a Brushless DC (BLDC) motor driven by a photovoltaic (PV) system, incorporating Maximum Power Point Tracking (MPPT) using the Perturb and Observe (P&O) method, Field-Oriented Control (FOC), and battery storage. The Proportional-Integral (PI) controller for motor speed regulation is optimized using the Bat Algorithm (BA), improving performance metrics such as settling time, steady-state error, rise time, and overshoot. Hall Effect sensors provide accurate rotor position and speed feedback, enabling precise commutation and control. The MPPT algorithm ensures maximum power extraction from the PV panel under varying sunlight conditions, while a DC-DC boost converter increases the voltage. to the necessary level for the BLDC motor. The battery storage system ensures continuous operation during periods of low solar input. Simulation results indicate that this design effectively harnesses solar energy, providing stable motor operation under changing load and irradiance conditions. It is well-suited for applications such as electric vehicles, water pumping systems, and robotics, offering a sustainable off-grid power solution for BLDC motor-driven systems

Accurate wind power forecasting is essential for the effective integration of wind energy into power grids. Yet, the inherent variability of wind and the intricate interplay of meteorological factors make prediction a challenging task. This study introduces a novel short-term wind power forecasting method, improving the traditional convolutional neural network and long short-term memory (CNN-LSTM) model through two significant innovations. First, we introduce a parallel convolutional architecture that employs both 1dimensional (1D) and 2-dimensional (2D) convolutions to simultaneously capture temporal patterns and inter-variable relationships in wind power data. This structure, inspired by Explainable-CNNs, enables more comprehensive feature extraction. Second, we integrate an attention mechanism that dynamically weights the importance of different input features and time steps, improving both forecast accuracy and model interpretability. The proposed model is evaluated using data from two wind farms in Croatia, comparing its performance against benchmark models including standard CNN-LSTM, LSTM, and gated recurrent unit (GRU) networks. Results demonstrate that our enhanced CNN-LSTM model achieves superior forecasting accuracy, with improvements in Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of 15% and 12% respectively, compared to the best-performing benchmark. Furthermore, the attention mechanism provides valuable insights into the relative importance of different features over time, offering a new level of interpretability in wind power forecasting models. This work contributes to the advancement of accurate and explainable wind power prediction, supporting more efficient renewable energy integration and grid management

This paper provides a detailed examination of speed control methods for induction motors, with a specific focus on the use of different pulse width modulation (PWM) techniques to achieve precise speed regulation and efficient motor operation. The study investigates the application of sinusoidal PWM (SPWM), third harmonic injection PWM (THPWM), space vector PWM (SVPWM), and selective harmonic elimination PWM (SHEPWM). The proposed hybrid PWM technique is analyzed and compared with existing PWM techniques in both open-loop and closed-loop control strategies. The incorporation of feedback mechanisms such as speed sensors to dynamically adjust the PWM signals has been considered. Through the adjustment of carrier signal frequency and modulation index, the study identifies the optimal PWM technique for minimizing total harmonic distortion (THD) and switching losses. The paper concludes with recommendations on the most effective PWM techniques for specific conditions

The rapid rise in electric vehicle (EV) adoption highlights the critical need for a reliable charging infrastructure to ensure the stability of power distribution networks. This research introduces a fuzzy inference system (FIS) designed to forecast daily EV loads essential for developing microgrids to meet the increasing demand for EVs. The present work considers four factors for FIS designing: travel distance, parking duration, battery state of charge (SoC), and expected arrival times at charging stations. By developing fuzzy logic rules for these variables, a probabilistic charging is generated, improving both the precision and adaptability of load forecasts. This study also explores the impact of future EV adoption on microgrid load demand, analyzing adoption rates of 53%, 68%, and 84%, providing crucial insights for planning microgrids. The discrepancy between estimated and actual EV loads is found to be 0.078, demonstrating a reduction in prediction error. This effectively mitigates uncertainties related to EV user behavior and supports the design of resilient and flexible microgrid systems

Predicting battery capacity is essential for enhancing battery management systems (BMSs), ensuring safety, and extending battery life. However, lithium-ion battery faces the challenge of performance degradation over the period due to electrochemical phenomena. It can be addressed with data-driven techniques to estimate the battery capacity and remaining useful life (RUL). The machine learning (ML) algorithm efficacy directly impacted by the data types. NASA and CALCE datasets are used to validate the applicability of ML algorithms. The dataset are divided into training and testing sets based on charged-discharged cycles. Pretraining datasets are tested in time series with forward prediction judgment of the data size to predict RUL. There may be an overfitting or underfitting problems in estimating capacity of the battery. However, such problems can be addressed with proper tuning of hyperparameters in time series model with, number of trees, maximum depth of the tree and splitting the data points. As data may be noisy or nonlinear, in most cases, RF prevents overfitting or underfitting by building multiple decision trees which reduces the variance and increases accuracy. RF achieves comparable prediction accuracy, even when trained on limited data as compared to existing data-driven techniques in terms of error metrics RMSE, MSE, MAPE, and R2. The findings highlight RF as a preferred choice with an average RMSE error reduced to 5.66E-16 and predict the battery RUL maximum error in four cycles to lithium-ion battery. These techniques may provide robustness to BMS in real-time applications. This is a preview of subscription content, log in via an institution