State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach-Reference-Cited by-同舟云学术

State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach

Published:2018-10 Issue: Volume:400 Page:242-255
ISSN:0378-7753
Container-title:Journal of Power Sources
language:en
Short-container-title:Journal of Power Sources

Author:

Chemali Ephrem^ORCID,Kollmeyer Phillip J.,Preindl Matthias,Emadi Ali

Funder

Canada Excellence Research Chairs, Government of Canada

Wisconsin Energy Institute at the University

NVIDIA Corporation

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Physical and Theoretical Chemistry,Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment

Reference40 articles.

1. Technological developments in batteries: a survey of principal roles, types, and management needs;Hu;IEEE Power Energy Mag.,2017

2. Advanced Electric Drive Vehicles;Emadi,2015

3. Electrochemical and electrostatic energy storage and management systems for electric drive vehicles: state-of-the-art review and future trends;Chemali;IEEE Journal of Emerging and Selected Topics in Power Electronics,2016

4. Special Report Energy and Air Pollution;World Energy Outlook,2016

5. State-of-charge and state-of-health estimation with state constraints and current sensor bias correction for electrified powertrain vehicle batteries;Malysz;IET Electr. Syst. Transp.,2016

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