Research Papers

Performance Comparison of Conventional and Intelligent method of Charge Estimation

This paper focuses on the implementation and performance comparison of a conventional and an intelligent method for estimation of SoC of a battery. Two different methods of estimation have been selected after careful study and literature review. The first method is Linear Kalman Filter (LKF), which is a conventional method, widely in use. The second method selected is Neural network using Feed Forward. The final results of both the methods are compared and studied to draw a conclusion. Both the methods have been implemented in MATLAB software. For Kalman Filter implementation, Thevenin circuit is modelled to achieve the needed equations. These equations are used to calculate the predict the error which the updates the Kalman gain. In Neural networks, the implementation comprises of training and testing. Mini batches have been taken for the training of the network along with Adam optimizer.

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IEEE 

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Best Paper Award
Smart IoMT Framework for Supporting UAV Systems with AI

The health monitoring system is one of the most innovative technologies that has gained traction in the Internet of Medical Things (IoMT). It allows the connection of multiple sensors and actuators that can capture and monitor the data through the web page or mobile application. IoMT technology not only provides communications but also will provide monitoring, recording, storage, and display. IoMT in healthcare is used for measuring the vital signs of the human body, which allows medical professionals to assess the well-being of a patient. The doctor may recommend lifestyle modifications, prescribe more tests, or diagnose a disorder according to the results. This paper illustrates the remote-control health monitoring system (HMS) with the integration of a UAV, which allows the doctor to access the data and analyze the patient data remotely. Thus, the proposed HMS-UAV system aims to measure the temperature, humidity, blood pressure, heart rate, and SpO 2 and stores the data on the UAV. Several sensors were thus used namely DHT11, MAX30102, Myoware and K24C16, and the Raspberry Pi camera. Reduced hospital stays and avoidance of readmissions are benefits of remote patient monitoring with IoMT-based UAVs. Contrary to its advantages, IoMT has flaws in information processing since a huge volume of data are needed to be handled in a single environment. One major novel inclusion in this work is to measure multiple parameters and provide a comparative analysis for all of them. Furthermore, the functionality of video recorded and stored is included where the doctor can surveil the patient.

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MDPI

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Raman Research Award
Battery management solutions for li-ion batteries based on artificial intelligence

The automobile industry is currently undergoing a paradigm change from conventional, diesel, and gasoline-powered vehicles to hybrid and electric vehicles of the second generation. Lithium-ion (Li-ion) batteries have sparked the automotive industry’s interest for quite some time. One of the most crucial components of an electric car is the battery management system (BMS). Since the battery pack is an electric vehicle's most significant and expensive component, it must be carefully monitored and controlled. The precise measurement and calculation of the many states of a Li-ion battery's cells, such as the State of Health (SOH) and State of Charge (SOC) is a difficult procedure as they cannot be monitored directly. This paper examines various methodologies and approaches for estimating the SOC and SOH of Li-ion batteries using Artificial Intelligent methods. Six machine learning algorithms are intensively utilized to investigate the Li-ion battery state estimation. The employed methods are linear, random forest, gradient boost, light gradient boosting (light-GBM), extreme gradient boosting (XGB), and support vector machine (SVM) regressors. In comparison to all other models employed in this study, the discharge prediction made using random forest exhibits significantly greater performance at a low loss of accuracy. For instance, with the highest R2-score of 0.999, the random forest regressor achieves only 0.0035, 0.0013, and 0.0097 for mean and median absolute error, and root means squared error (RMSE), respectively. We showed that the state estimation of Li-ion batteries can be precisely predicted using AI methods, which can be combined with a battery management system to improve electric vehicle performance.

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Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future

Solar energy has emerged as a vital renewable alternative to fossil fuels, enhancing environmental sustainability in response to the pressing need to reduce carbon emissions. However, the integration of solar power into the electrical grid faces challenges due to its unpredictable nature, as a result of solar energy production variability. This research presents an advanced Explainable Artificial Intelligence (XAI) framework to explicate machine learning models decision-making processes, thereby improving the predictability and management of solar energy distribution. The influence of critical parameters such as solar irradiance, module temperature, and ambient temperature on energy yield is studied using the Local Interpretable Model-Agnostic Explainer (LIME). Rigorous testing using four advanced regression models identified Random Forest Regressor as the superior model, with an R2 score of 0.9999 and a low Root Mean Square Error (RMSE) of 0.0061. Furthermore, Partial Dependency Plots (PDP) are used to emphasize the intricate dependencies and interactions among features in the dataset. The application of XAI techniques for solar power generation extends beyond explainability, addressing challenges due to various parameters in solar radiation pattern analysis, error estimation in solar performance, degradation of the battery function, and also provides interpretable insights for enhancing the lifespan of solar panels, contributing to advancements in sustainable energy technologies. The results of this study show how XAI has the potential to transform power system management (PSM) and strategic planning, propelling us toward a future of energy that is more resilient, efficient, and environmentally friendly.

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Novel lane and object detection technique using visual camera

The project aims to implement a real-time lane, object and pedestrian detection system using visual input froma camera mounted on the front of the vehicle. The project is developed using OpenCV based YOLO libraries and Tensorflow. Various objects that appear in the feed of the camera are identified, numbered and labelled so that the self-driving vehicle can maneuver accordingly. To ensure that proposed system is unique in its approach and performs better than existing methodologies, a novel approach was considered wherein the detection of each parameter is segmented, cascaded and simultaneously executed. This novel approach ensures that detection of each parameter remains independent of the other, and the performance and efficiency is at maximum making the system ideal for autonomous vehicles and semi autonomous vehicles.

AIP Publishing

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Upcoming Research Papers

Ammonia Based Thermal Exchange Charging System For Hybrid Electric Vehicles

Conventional hybrid electric vehicle technologies combine the benefits of electric motors and IC engines with either a combined drive train or a serially powered one. As we know, maximum energy loss is in the form of heat, either from the engine, the motor, or from the battery packs. This heat could be harnessed to generate useful energy not only to decrease the heat dissipated but also to boost the entire system’s efficiency. The idea proposed in this paper explores the use of ammonia as a working fluid to absorb heat from these parts of the vehicle, power a small turbine, which in turn charges the hybrid vehicle’s batteries. Since it is a completely closed loop system, the loss in energy, working fluid or pressure is minimal, hence a highly effective method of heat transfer and power generation is achieved. The ammonia is recycled by a pump mechanism that is powered by the pressure difference caused by the exhaust fumes of the vehicle's Internal Combustion engine. Water can be used to produce energy from the heat generated by a diesel engine's exhaust gas, however, for small engines and at varying load operations, the waste heat gas temperature is insufficient to heat the steam that is to be superheated. Ammonia is used as a working fluid which has the ability to work at low exhaust temperatures.