ESTIMATION OF STATE OF CHARGE FOR LITHIUM ION BATTERY USING IMPROVED GATE RECURRENT NEURAL NETWORK FOR ELECTRIC VEHICLE DRIVING PROCESS

Li-ion battery packs, which are increasingly being utilised in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Unmanned Aerial Vehicles (UAV), and smart grid systems, require accurate estimation of State of Charge (SOC).The SOC, often known as the actual quantity of battery energy remaining,...

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Bibliographic Details
Published inNeuroQuantology Vol. 20; no. 6; p. 8886
Main Authors M SURENDAR P PRADEEPA, Mohan, M
Format Journal Article
LanguageEnglish
Published Bornova Izmir NeuroQuantology 01.01.2022
Subjects
Accuracy
Algorithms
Alternative energy sources
Batteries
Electric charge
Electric vehicles
Energy resources
Energy storage
Engineering
Hybrid electric vehicles
Lithium
Lithium-ion batteries
Network topologies
Neural networks
Parameter estimation
Rechargeable batteries
Recurrent neural networks
Renewable resources
Smart grid
State of charge
Unmanned aerial vehicles
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Summary:Li-ion battery packs, which are increasingly being utilised in Electric Vehicles (EV), Hybrid Electric Vehicles (HEV), Unmanned Aerial Vehicles (UAV), and smart grid systems, require accurate estimation of State of Charge (SOC).The SOC, often known as the actual quantity of battery energy remaining, is a critical factor in guaranteeing the performance and consistency of lithium-ion battery technology (LIBT)
ISSN:1303-5150
DOI:10.14704/nq.2022.20.6.NQ22874