Chaotic Harris Hawks Optimization Algorithm for Electric Vehicles Charge Scheduling V. Manoj Kumar, C. Bharatiraja, Ali ELrashidi, Kareem M. AboRas Energy Reports, 2024 Electric Vehicle (EV) technology and migration are hindered by battery sizing, short driving ranges, and optimal operations. This article focuses on developing a strategy for scheduling EV charging in a specific region, addressing waiting time, charging time, and uneven scheduling due to unevenly distributed charging stations (CS). The proposed approach optimizes CS using separate queues for different levels, reducing waiting time and costs during peak hours. Which considers trade-offs between time-aware fairness and overall waiting time, and factors like reachability, battery state of charge, depth of discharge limits, and charging rate constraints. A bi-objective formulation and online scheduling algorithm based on dynamic schedulable time, energy demand fluctuation and user’s prioritization are proposed. The aim is to allocate a charging station to each EV by considering travel needs and battery specifics, with the objective of minimizing travel time, queue time, recharging time, and energy costs. To achieve this, the scheduling system utilizes the Chaotic Harris Hawks Optimization (CHHO), an enhanced iteration of the previously discussed metaheuristic, the Harris Hawk Optimization. Validation of the system is conducted through Vehicular Ad-hoc Network (VANET) simulation and comparison with alternative algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer and Random allocation. The outcomes demonstrate noteworthy decreases in travel time, queue time, recharging time, and energy costs, all while adhering to set constraints.
Demand side management using optimization strategies for efficient electric vehicle load management in modern power grids Manoj Kumar V., Bharatiraja Chokkalingam, Devakirubakaran S. Plos One, 2024 The Electric Vehicle (EV) landscape has witnessed unprecedented growth in recent years. The integration of EVs into the grid has increased the demand for power while maintaining the grid’s balance and efficiency. Demand Side Management (DSM) plays a pivotal role in this system, ensuring that the grid can accommodate the additional load demand without compromising stability or necessitating costly infrastructure upgrades. In this work, a DSM algorithm has been developed with appropriate objective functions and necessary constraints, including the EV load, distributed generation from Solar Photo Voltaic (PV), and Battery Energy Storage Systems. The objective functions are constructed using various optimization strategies, such as the Bat Optimization Algorithm (BOA), African Vulture Optimization (AVOA), Cuckoo Search Algorithm, Chaotic Harris Hawk Optimization (CHHO), Chaotic-based Interactive Autodidact School (CIAS) algorithm, and Slime Mould Algorithm (SMA). This algorithm-based DSM method is simulated using MATLAB/Simulink in different cases and loads, such as residential and Information Technology (IT) sector loads. The results show that the peak load has been reduced from 4.5 MW to 2.6 MW, and the minimum load has been raised from 0.5 MW to 1.2 MW, successfully reducing the gap between peak and low points. Additionally, the performance of each algorithm was compared in terms of the difference between peak and valley points, computation time, and convergence rate to achieve the best fitness value.
Mitigation of Complexity in Charging Station Allocation for EVs Using Chaotic Harris Hawks Optimization Charge Scheduling Algorithm V. Manoj Kumar, Bharatiraja Chokkalingam, Lucian Mihet-Popa IEEE Access, 2023 The development of EV technology and EV migration is limited by various factors such as sizing of batteries, short driving ranges, optimal operations, and so on. The EV charging faces many difficulties such as waiting time, charging time, uneven charge scheduling, and uneven distributed charging stations. In Charging Station (CS), EVs usually spend much time in queues, mainly during peak hours of charging. Therefore, building a well-established charging station network should be derived from charging demand and proper charge scheduling to assist EVs for getting charged with less cost, less waiting time. Also, it should reduce the number of vehicles scheduling during the peak load time. This work aims to design a scheduling system for EV charging using an optimization strategy of Chaotic Harris Hawks optimization (CHHO) which reduces the total time spent on charging station and the distance of EV origin to destination. CHHO is authenticated using Vehicular Ad-hoc Network (VANET) simulation, and the performances are compared with algorithms Exponential Harris Hawk Optimization, Grey Wolf Optimizer, First in First Out and Random Allocation to demonstrate the efficacy of our technique. The proposed CHHO-based scheduling system yields better performance with the maximum remaining energy and significantly cuts the average travel time, and improves the utilization rate of EVs in charging stations compared to other algorithms. A detailed result and discussions on different case studies by varying number of vehicles and number of charging stations and the corresponding average waiting time were obtained and presented in this paper.
Path Planning of an UAV with the Help of Lidar for Slam Application C Aakash, V Manoj Kumar Iop Conference Series Materials Science and Engineering, 2020 Today’s world there will be always a value for path planning. Be it the use of mobile robots such as UAVs, UGVs, USVs, etc., everything functions based on SLAM input. The input parameter can be extracted by any kind of sensors such as Kinect, LiDAR, etc. Here I am using RPLIDAR(LiDAR) as Sensor for UAV path generation. By getting a single Plan reading from lidar I will generate 3D mapping by a gazebo and visualize it by rviz in ROS. Here we use ROS as an interface of robot and sensor.
Deep Learning - A Review Vamsi Madhav Kota, V Manoj Kumar, C Bharatiraja Iop Conference Series Materials Science and Engineering, 2020 In recent years, tech giants in various parts are showing Curiosity on Artificial Intelligence by investment on the project that can be a game-changer for both corporate and researchers. A company such as Google, Baidu, and Yandex have already started their multimillion-dollar project in this pitch. This article presents the latest progress and also tries to paint a predictive picture of future research directions and developments in the domain of deep learning. Each of the said research directions and avenues is analyzed and summarized in a brief yet concise manner in this article. Initially, an outline of the three elementary models of deep learning that including multilayer perceptions and perceptrons, convolutional neural networks and recurrent neural networks. Building on the bases of foundation, further analyses of the emerging new types of convolutional neural networks and recurrent neural networks are also undertaken in this current study. This article then summarizes deep learning and its applications in the domain of artificial intelligence, counting speech processing, computer vision, and natural language processing. Finally, the purpose of deep learning is discussed. The current article also delves a little deeper into the inner workings of the neural networking architecture associated with object detection and computer vision.
Autonomous navigation of a mobile robot in dynamic indoor environments using SLAM and reinforcement learning C C E Chewu, V Manoj Kumar Iop Conference Series Materials Science and Engineering, 2018 The recent advances in robotics has resulted in a more convenient use of mobile robots in alications such as assisting the disabled, deliveries and domestic purposes. The main challenge faced by mobile robots is navigation in a dynamic environment, which is path planning for dynamic obstacle avoidance. This paper proposes a novelty method for solving the path planning problem for mobile robots posed by dynamic obstacles based on SLAM (Simultaneous Localisation and Maing) algorithm and Reinforcement Learning. The algorithms implemented relied on the Kinect sensor for maing and rotary encoder for localisation of the robot in the map. The SLAM algorithm implemented resulted in a mean error metric of 4.07%. The modified Q-learning algorithm implemented in this paper allowed the mobile robot to avoid dynamic obstacles by re-planning the path to find another optimal path different from the previously set global optimal path. From the investigation, it was shown that it is possible for a robot to navigate in a dynamic using the Reinforcement Learning technique.
Developing a transfer function based firmware for a gripper sensor P Sandeep Narayanan, V Manoj Kumar, S Arokya Augustin Iop Conference Series Materials Science and Engineering, 2018 Sensors a system whose sole purpose is to sense and give the output. It maybe of anything from normal measuring of speed, temperature to the in-depth sensing of motions or strains. One such type of sensor is being fabricated to measure the value of stress using strain gauges. Strain gauge is a sensor which has change in values when external force is applied Previously stress is found on a beam near the point of maximum bending but in here the stress are known from an object so as it helps in gripping of the object. The main purpose is to implement in the robot grippers. Considering it as a sensor the size is in centimeter range. From the design aspect it is of two parts namely base and struts. The struts are arranged in a regular array on the base plate. The major consideration would be the base as a fixed part and the pressure is applied on the strut top side. Simulation of design are done in ANSYS with consideration. It is done to determine the material used to fabricate. The fabrication will be done and the stress and strain data will be taken.
An experimental study and comparitive validation of macrolayer thickness in nucleate pool boiling for horizontal copper tube heater K C Udaiyakumar, U Poongundran, K Yoganand, V Manoj Kumar Iop Conference Series Materials Science and Engineering, 2018 Nucleate Pool boiling is characterised by the progression of several nucleation sites (bubble formation), which surge from distinct points on a surface, whose temperature is slightly above the liquids. To investigate and evaluate this, a pool boiling setup was fabricated with a horizontal copper tube heater of 28mm diameter by imposing cartridge heater. An analytical expression, suggested by literature helps to determine the thickness of macrolayer based on bubble diameter was used. The macrolayer thickness for water was found by measuring the bubble diameter by using Photographic and CAD method. Experiment was carried out in a stainless steel container insulated with Teflon cover to observe the bubble growth and bubble departure characteristics for the heat flux range of 1000-42,000 W/m2. The bubble diameter were measured and the measured parameters were been used to determine the initial layer thickness, macro layer thickness and critical heat flux and validated through various models. The observed and calculated values are in good agreement as reported in various literature.
Multibody dynamic simulation of a hyper redundant robotic manipulator using ADAMS ansys interaction Anand Nagarajan, S.K. Rajesh Kanna, V. Manoj Kumar 2017 International Conference on Algorithms Methodology Models and Applications in Emerging Technologies Icammaet 2017, 2017 A Continuum type hyper redundant robotic manipulator is a special kind of manipulator inspired by the anatomy and operation of an elephant trunk. A novel type multi-sectioned arm with a continuous back bone curve is considered for the multibody dynamic simulation using ADAMS (Automatic Dynamic Analysis for Multi-body Systems) software. In this paper, the construction and working of the bio-inspired arm is discussed, followed by the numerical simulation of the shape of the back bone curve of each of the section using ADAMS — Ansys interaction. The geometric non-linearity experienced by the arm during actuation is simplified by predicting the curvature of each section and then coupling them end to end to obtain the overall shape of the backbone curve. For this work, ADAMS command language is used to create a research platform to test the kinematics of the arm with user specified parameters. The robot end effector position can be obtained for any geometry and end load using this platform.
Robot arm control using image processing and matlab for simple writing by human gestures International Journal of Mechanical Engineering and Technology, 2017
Optimized water pumping system using Arduino for home automation International Journal of Mechanical Engineering and Technology, 2017
A RRP configuration robot arm for drawing application Journal of Chemical and Pharmaceutical Sciences, 2016
