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Found 9 papers, 0 papers with code
A Deep Learning Approach Towards Generating High-fidelity Diverse Synthetic Battery Datasets
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.
GAETS: A Graph Autoencoder Time Series Approach Towards Battery Parameter Estimation
In this paper we employ Graph Neural Networks for battery parameter estimation, we introduce a unique graph autoencoder time series estimation approach.
An Accurate Smartphone Battery Parameter Calibration Using Unscented Kalman Filter
Most of the remote devices, part of the IoT network, such as smartphones, data loggers and wireless sensors are battery powered.
Overcoming limited battery data challenges: A coupled neural network approach
However, limited availability of open-source diverse datasets has stifled the growth of this field, and is a problem largely ignored in literature.
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.
Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles
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.
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.
A Temporal Convolution Network Approach to State-of-Charge Estimation in Li-ion Batteries
It is crucial to accurately estimate SOC to determine the available range in an EV while it is in use.
Quasar Detection using Linear Support Vector Machine with Learning From Mistakes Methodology
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.
