State of Charge Estimation of Lithium-ion Batteries Using Artificial Intelligence Based on Entropy and Enthalpy Variation
- 2 March 2023
- book chapter
- Published by Springer Nature
- p. 747-756
Abstract
No abstract availableKeywords
This publication has 25 references indexed in Scilit:
- Introduction to machine learning: k-nearest neighborsAnnals of Translational Medicine, 2016
- Thermodynamics of Lithium-Ion Batteries2014
- Battery state of the charge estimation using Kalman filteringJournal of Power Sources, 2013
- The State of Charge Estimating Methods for Battery: A ReviewISRN Applied Mathematics, 2013
- Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental ApproachEnergies, 2011
- Thermodynamics of Electrode Materials for Lithium‐Ion Batteries2009
- An introduction to decision tree modelingJournal of Chemometrics, 2004
- A review of state-of-charge indication of batteries by means of a.c. impedance measurementsJournal of Power Sources, 2000
- A general regression neural networkIEEE Transactions on Neural Networks, 1991
- Ridge Regression: Biased Estimation for Nonorthogonal ProblemsTechnometrics, 1970