Real-time monitoring of battery state of charge using artificial neural networks-Reference-Cited by-同舟云学术

Real-time monitoring of battery state of charge using artificial neural networks

Published:2022-04-27 Issue:1 Volume:43 Page:7182-7196
ISSN:0143-0750
Container-title:International Journal of Ambient Energy
language:en
Short-container-title:International Journal of Ambient Energy

Author:

Bhagavatula Sai Vasudeva¹,Yellamraju Venkata Rupesh Bharadwaj¹,Eltem Karthik Chandra¹,Shashank P. N.¹,Bobba Phaneendra Babu¹^ORCID,Kosaraju Satyanarayana²^ORCID,Marati Naveen Kumar³

Affiliation:

1. Department of Electrical & Electronics Engineering, GRIET, Hyderabad, India

2. Department of Mechanical Engineering, GRIET, Hyderabad, India

3. Architect, Electric Vehicle CoE, Wipro Limited, Bangalore, India

Publisher

Informa UK Limited

Subject

Building and Construction,Renewable Energy, Sustainability and the Environment

Link

https://www.tandfonline.com/doi/pdf/10.1080/01430750.2022.2049872

Reference35 articles.

1. Cave Pearl Data Logger: A Flexible Arduino-Based Logging Platform for Long-Term Monitoring in Harsh Environments

2. State-of-charge sequence estimation of lithium-ion battery based on bidirectional long short-term memory encoder-decoder architecture

3. A Novel Model-Based Voltage Construction Method for Robust State-of-Health Estimation of Lithium-Ion Batteries

4. A Two-Step Parameter Optimization Method for Low-Order Model-Based State-of-Charge Estimation

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