A5008142377- Advanced Control Systems Design
- Metaheuristic Optimization Algorithms Research
- Advanced Battery Technologies Research
- Frequency Control in Power Systems
- Sensorless Control of Electric Motors
- Wireless Body Area Networks
- Solar Radiation and Photovoltaics
- Advanced DC-DC Converters
- Control Systems and Identification
- ECG Monitoring and Analysis
- IoT and Edge/Fog Computing
- Multilevel Inverters and Converters
- Fault Detection and Control Systems
- Industrial Automation and Control Systems
- Advancements in Battery Materials
- Electric Motor Design and Analysis
- Photovoltaic System Optimization Techniques
- Iterative Learning Control Systems
- Energy Load and Power Forecasting
- Electric Vehicles and Infrastructure
- Extremum Seeking Control Systems
Kalasalingam Academy of Research and Education
2016-2024
The durability and reliability of battery management systems in electric vehicles to forecast the state charge (SoC) is a tedious task. As process degradation usually non-linear, it extremely cumbersome work predict SoC estimation with substantially less degradation. This paper presents lithium-ion using six machine learning algorithms for application. employed are artificial neural network (ANN), support vector (SVM), linear regression (LR), Gaussian (GPR), ensemble bagging (EBa), boosting...
Estimating state-of-charge (SoC) for Lithium-ion (Li-ion) battery is the key issue in management system (BMS). Now a days they are widely used many industrial applications, especially electric vehicle (EV). SoC estimation BMS plays an important role ensuring safety, reliability and better life EV. This work describes digital-twin model parameter identification extended kalman filter (EKF) estimating pack's parameters SoC. State space of EV developed it EKF estimation. With this present...
This paper presents the closed-loop control (CLC) simulation for Brushless Direct Current (BLDC) motor used in electric vehicle (EV) application. There are many methods available to BLDC motor, here a comparative study between conventional and auto-tuned optimal controller method PID is reported. To ensure quality of work, parameters estimated from real-time by experiments it its performance thoroughly. From this concluded that has good track efficiency achieving target speed, torque current...
This investigation presents an optimisation-driven fractional order PID (FOPID) controller design methodology for brushless direct current (BLDC) motor speed control electric vehicle applications. Though the introduction of terms provides additional flexibility, their optimal selection is important achieving desired performance. For this purpose, uses two evolutionary optimisation approaches-real coded genetic algorithm (RGA) and bio-geography based (BBO). In to illustrate improvements...
This study presents an extremum seeking‐proportional–integral and derivative (ES‐PID) controller design for brushless direct current motors its implementation in electric vehicles. The ES‐PID aims to simultaneously maintain a speed set‐point reduce torque ripples the presence of load‐torque disturbances. proposed combines simplicity PID with seeking approach, model‐free optimisation thereby resulting optimal that can be realised on simple hardware (with limited computing power memory). In...
With the current transport, environment becomes polluted. Electric Vehicle, which is powered by batteries, best option for reducing environmental pollution and fuel shortage. The Vehicle's primary element motor its controller. Brushless Direct Current used in most of Vehicle. high performance, torque-to-weight ratio, brushless design make BLDC usable. This paper uses PI controller to investigate an Electronic Control Unit (ECU) three-phase motor. To control ECU's parameter, NSGA-II...
This investigation presents an optimisation-driven fractional order PID (FOPID) controller design methodology for brushless direct current (BLDC) motor speed control electric vehicle applications. Though the introduction of terms provides additional flexibility, their optimal selection is important achieving desired performance. For this purpose, uses two evolutionary optimisation approaches-real coded genetic algorithm (RGA) and bio-geography based (BBO). In to illustrate improvements...