arXiv subjects

Janamejaya Channegowda

Publications and source records attributed to Janamejaya Channegowda.

11 recordsLinked to original sources

arXiv2023

A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets

Recent surge in the number of Electric Vehicles have created a need to develop inexpensive energy-dense Battery Storage Systems. Many countries across the planet have put in place concrete measures to reduce and subsequently limit the number of vehicles powered by fossil fuels. Lithium-ion based batteries are presently dominating the electric automotive sector. Energy research efforts are also focussed on accurate computation of State-of-Charge of such batteries to provide reliable vehicle range estimates. Although such estimation algorithms provide precise estimates, all such techniques available in literature presume availability of superior quality battery datasets. In reality, gaining access to proprietary battery usage datasets is very tough for battery scientists. Moreover, open access datasets lack the diverse battery charge/discharge patterns needed to build generalized models. Curating battery measurement data is time consuming and needs expensive equipment. To surmount such limited data scenarios, we introduce few Deep Learning-based methods to synthesize high-fidelity battery datasets, these augmented synthetic datasets will help battery researchers build better estimation models in the presence of limited data. We have released the code and dataset used in the present approach to generate synthetic data. The battery data augmentation techniques introduced here will alleviate limited battery dataset challenges.

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arXiv2022

GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation

Lithium-ion batteries are powering the ongoing transportation electrification revolution. Lithium-ion batteries possess higher energy density and favourable electrochemical properties which make it a preferable energy source for electric vehicles. Precise estimation of battery parameters (Charge capacity, voltage etc) is vital to estimate the available range in an electric vehicle. Graph-based estimation techniques enable us to understand the variable dependencies underpinning them to improve estimates. In this paper we employ Graph Neural Networks for battery parameter estimation, we introduce a unique graph autoencoder time series estimation approach. Variables in battery measurements are known to have an underlying relationship with each other in a certain correlation within variables of interest. We use graph autoencoder based on a non-linear version of NOTEARS as this allowed us to perform gradient-descent in learning the structure (instead of treating it as a combinatorial optimisation problem). The proposed architecture outperforms the state-of-the-art Graph Time Series (GTS) architecture for battery parameter estimation. We call our method GAETS (Graph AutoEncoder Time Series).

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arXiv2021

An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter

Internet of Things (IoT) applications have opened up numerous possibilities to improve our lives. Most of the remote devices, part of the IoT network, such as smartphones, data loggers and wireless sensors are battery powered. It is vital to collect battery measurement data (Voltage or State-of-Charge (SOC)) from these remote devices. Presence of noise in these measurements restricts effective utilization of this dataset. This paper presents the application of Unscented Kalman Filter (UKF) to mitigate measurement noise in smartphone dataset. The simplicity of this technique makes it a constructive approach for noise removal. The datasets obtained after noise removal could be used to improve data-driven time series forecasting models which aid to accurately estimate critical battery parameters such as SOC. UKF was tested on noisy charge and discharge dataset of a smartphone. An overall Mean Squared Error (MSE) of 0.0017 and 0.0010 was obtained for Voltage charge and discharge measurements. MSE for SOC charge and discharge data measurements were 0.0018 and 0.0010 respectively.

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arXiv2021

Overcoming limited battery data challenges: A coupled neural network approach

The Electric Vehicle (EV) Industry has seen extraordinary growth in the last few years. This is primarily due to an ever increasing awareness of the detrimental environmental effects of fossil fuel powered vehicles and availability of inexpensive Lithium-ion batteries (LIBs). In order to safely deploy these LIBs in Electric Vehicles, certain battery states need to be constantly monitored to ensure safe and healthy operation. The use of Machine Learning to estimate battery states such as State-of-Charge and State-of-Health have become an extremely active area of research. However, limited availability of open-source diverse datasets has stifled the growth of this field, and is a problem largely ignored in literature. In this work, we propose a novel method of time-series battery data augmentation using deep neural networks. We introduce and analyze the method of using two neural networks working together to alternatively produce synthetic charging and discharging battery profiles. One model produces battery charging profiles, and another produces battery discharging profiles. The proposed approach is evaluated using few public battery datasets to illustrate its effectiveness, and our results show the efficacy of this approach to solve the challenges of limited battery data. We also test this approach on dynamic Electric Vehicle drive cycles as well.

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arXiv2020

Single Stage PFC Flyback AC-DC Converter Design

This paper discusses a 100 W single stage Power Factor Correction (PFC) flyback converter operating in boundary mode constant ON time methodology using a synchronous MOS-FET rectifier on the secondary side to achieve higher efficiency. Unlike conventional designs which use two stage approach such as PFC plus a LLC resonant stage or a two stage PFC plus flyback, the proposed design integrates the PFC and constant voltage regulation in a single stage without compromising the efficiency of the converter. The proposed design is advantageous as it has a lower component count. A design of 100 W flyback operating from universal input AC line voltage is demonstrated in this paper. The experimental results show that the power factor (PF) is greater than 0.92 and total harmonic distortion (iTHD) is less than 20% for a load varying from 25 % to 100 %. The experimental results show the advantages of a single stage design.

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arXiv2020

Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles

Electric Vehicles (EVs) are rapidly increasing in popularity as they are environment friendly. Lithium Ion batteries are at the heart of EV technology and contribute to most of the weight and cost of an EV. State of Charge (SOC) is a very important metric which helps to predict the range of an EV. There is a need to accurately estimate available battery capacity in a battery pack such that the available range in a vehicle can be determined. There are various techniques available to estimate SOC. In this paper, a data driven approach is selected and a Nonlinear Autoregressive Network with Exogenous Inputs Neural Network (NARXNN) is explored to accurately estimate SOC. NARXNN has been shown to be superior to conventional Machine Learning techniques available in the literature. The NARXNN model is developed and tested on various EV Drive Cycles like LA92, US06, UDDS and HWFET to test its performance on real world scenarios. The model is shown to outperform conventional statistical machine learning methods and achieve a Mean Squared Error (MSE) in the 1e-5 range.

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arXiv2020

Generalized Circuit Averaging Technique for Two Switch DC-DC Converters

Cuk and SEPIC are some of the important DC-DC converters used for charging batteries. In this paper, a generalized circuit averaging technique is employed for Cuk and SEPIC converters. The derived equations are used to obtain the frequency response of open loop transfer function. The ratio of perturbed output voltage to duty cycle ($G_{vd}$) is simulated using LTSpice software package. The derived averaged models of the converters aids in faster and simpler simulation. The behavior of the converters in CCM and DCM was also simulated. The derived expressions can be generalized to power converters with two switches.

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arXiv2020

Carrier-Based Modulation Schemes Based on Symmetric Switching Patterns for Three-Phase Three-Switch Rectifier

Electric Vehicle (EV) chargers are the need of the hour as EVs have invaded the commercial automobile market. The Three-Phase Three-Switch (TPTS) converter has features which make it ideal to act as the charging station because of its high efficiency at peak power rating. Traditionally, control of the TPTS converter has been achieved by the Space Vector (SV) based modulation scheme whose implementation is burdensome due to the trigonometric calculations involved. This paper introduces two simplified carrier based modulation schemes, which greatly reduce the implementation difficulty, as they require minimum mathematical computations. A quantitative comparison is made between SV scheme available in literature with the simplified scheme proposed here. The methodology followed to obtain the carrier waves is also provided. All the modulation schemes are validated with the help of a 1 kW hardware prototype.

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arXiv2020

Thermal Analysis of PEM Fuel Cell and Lithium Ion Battery Pack in Confined Space

Hybrid energy storage systems (HESS) have carved a niche in the industry. HESS improve the system efficiency, reduce the overall cost and increase the lifespan of the system. The proton exchange membrane (PEM) fuel cell is hybridized with Li-ion batteries (LIB) for vehicular applications, robotic applications etc. In applications which have geometrical space constraints, the temperature of the energy storage elements is influenced by convective heat transfer. In this paper the thermal analysis of the geometry of PEM-LIB hybrid system is carried out using COMSOL Multiphysics Software package for different discharge rates (C rates) of the LIB and different voltages of the PEM cell. The additional rise in temperature of the LIB pack when placed in close proximity with PEM cell was in the range of 0.03-0.6$^0$C at 4C. The cell temperature of the LIB pack increased with increase in C rate and decrease in PEM cell voltage.

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arXiv2020

A Temporal Convolution Network Approach to State-of-Charge Estimation in Li-ion Batteries

Electric Vehicle (EV) fleets have dramatically expanded over the past several years. There has been significant increase in interest to electrify all modes of transportation. EVs are primarily powered by Energy Storage Systems such as Lithium-ion Battery packs. Total battery pack capacity translates to the available range in an EV. State of Charge (SOC) is the ratio of available battery capacity to total capacity and is expressed in percentages. It is crucial to accurately estimate SOC to determine the available range in an EV while it is in use. In this paper, a Temporal Convolution Network (TCN) approach is taken to estimate SOC. This is the first implementation of TCNs for the SOC estimation task. Estimation is carried out on various drive cycles such as HWFET, LA92, UDDS and US06 drive cycles at 1 C and 25 °Celsius. It was found that TCN architecture achieved an accuracy of 99.1%.

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arXiv2020

Quasar Detection using Linear Support Vector Machine with Learning From Mistakes Methodology

The field of Astronomy requires the collection and assimilation of vast volumes of data. The data handling and processing problem has become severe as the sheer volume of data produced by scientific instruments each night grows exponentially. This problem becomes extensive for conventional methods of processing the data, which was mostly manual, but is the perfect setting for the use of Machine Learning approaches. While building classifiers for Astronomy, the cost of losing a rare object like supernovae or quasars to detection losses is far more severe than having many false positives, given the rarity and scientific value of these objects. In this paper, a Linear Support Vector Machine (LSVM) is explored to detect Quasars, which are extremely bright objects in which a supermassive black hole is surrounded by a luminous accretion disk. In Astronomy, it is vital to correctly identify quasars, as they are very rare in nature. Their rarity creates a class-imbalance problem that needs to be taken into consideration. The class-imbalance problem and high cost of misclassification are taken into account while designing the classifier. To achieve this detection, a novel classifier is explored, and its performance is evaluated. It was observed that LSVM along with Ensemble Bagged Trees (EBT) achieved a 10x reduction in the False Negative Rate, using the Learning from Mistakes methodology.

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