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Battery Pack Data Compression and Reconstruction for Electrical Vehicles Using Polynomial Regression

https://doi.org/10.2139/ssrn.4753869
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Applied Linear Regression
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Efficient Lossy Compression for IoT Using SZ and Reconstruction with 1D U-Net
inferredcites →https://doi.org/10.1007/s40789-022-00494-0openalex-extracted
Critical review and functional safety of a battery management system for large-scale lithium-ion battery pack technologies
inferredcites →https://doi.org/10.1016/j.apenergy.2016.08.140openalex-extracted
Recording frequency optimization for massive battery data storage in battery management systems
inferredcites →https://doi.org/10.1016/j.enconman.2014.12.076openalex-extracted
Dynamical modeling procedure of a Li-ion battery pack suitable for real-time applications
inferredcites →https://doi.org/10.1016/j.energy.2022.125802openalex-extracted
An adaptive capacity estimation approach for lithium-ion battery using 10-min relaxation voltage within high state of charge range
inferredcites →https://doi.org/10.1016/j.est.2020.101252openalex-extracted
Massive battery pack data compression and reconstruction using a frequency division model in battery management systems
inferredcites →https://doi.org/10.1016/j.est.2022.104144openalex-extracted
Early Quality Classification and Prediction of Battery Cycle Life in Production Using Machine Learning
inferredcites →https://doi.org/10.1016/j.etran.2022.100186openalex-extracted
Compressing and reconstructing the voltage data for lithium-ion batteries using model migration and un-equidistant sampling techniques
inferredcites →https://doi.org/10.1016/j.iot.2022.100516openalex-extracted
Lossy Data Compression for IoT Sensors: A Review
inferredcites →https://doi.org/10.1016/j.proeng.2012.09.545openalex-extracted
Modelling using Polynomial Regression
inferredcites →https://doi.org/10.1016/j.rser.2021.110801openalex-extracted
Towards the future of smart electric vehicles: Digital twin technology
inferredcites →https://doi.org/10.1038/s41467-022-29837-wopenalex-extracted
Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation
inferredcites →https://doi.org/10.1109/access.2019.2942213openalex-extracted
State of Charge Estimation for Lithium-Ion Batteries Using Model-Based and Data-Driven Methods: A Review
inferredcites →https://doi.org/10.1109/ccdc.2019.8833008openalex-extracted
SOC Estimation of Lithium Battery Based on N-2RC Model in Electric Vehicle
inferredcites →https://doi.org/10.1109/ciot57267.2023.10084883openalex-extracted
Introduction of a Cloud Computing Architecture for the Condition Monitoring of a Reconfigurable Battery System for Electric Vehicles
inferredcites →https://doi.org/10.1109/ictcs.2019.8923112openalex-extracted
Compression Techniques Used in Iot: A Comparitive Study
inferredcites →https://doi.org/10.1109/tte.2022.3170359openalex-extracted
Hybrid Electrical Circuit Model and Deep Learning-Based Core Temperature Estimation of Lithium-Ion Battery Cell
inferredcites →https://doi.org/10.3390/batteries2020007openalex-extracted
Fast Characterization Method for Modeling Battery Relaxation Voltage
inferredcites →https://doi.org/10.3390/wevj11040077openalex-extracted
From Cell to Battery System in BEVs: Analysis of System Packing Efficiency and Cell Types
inferredcites →https://doi.org/10.17632/4fx8cjprxmopenalex-snapshot
Turnigy Graphene 5000mAh 65C Li-ion Battery Data
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