System Identification for Battery State Prediction and Lifespan Estimation-Reference-Cited by-同舟云学术

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System Identification for Battery State Prediction and Lifespan Estimation

Published:2024 Issue:4 Volume:58 Page:186-191
ISSN:2405-8963
Container-title:IFAC-PapersOnLine
language:en
Short-container-title:IFAC-PapersOnLine

Author:

Li Chenyi,Zhang Long

Publisher

Elsevier BV

Reference16 articles.

1. Online Condition Monitoring of Battery Systems With a Nonlinear Estimator;Ablay;IEEE Transactions on Energy Conversion,2014

2. Parametrization of physics-based battery models from input–output data: A review of methodology and current research;Andersson;Journal of Power Sources,2022

3. Systematic derivation and validation of a reduced thermal-electrochemical model for lithium-ion batteries using asymptotic methods;Brosa Planella;Electrochimica Acta,2021

4. Comparative study of reduced order equivalent circuit models for on-board state-of-available-power prediction of lithium-ion batteries in electric vehicles;Farmann;Applied Energy,2018

5. Towards the swift prediction of the remaining useful life of lithium-ion batteries with end-to-end deep learning;Hong;Applied Energy,2020

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