Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming-Reference-Cited by-同舟云学术

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Learning human-understandable models for the health assessment of Li-ion batteries via Multi-Objective Genetic Programming

Published:2019-11 Issue: Volume:86 Page:1-10
ISSN:0952-1976
Container-title:Engineering Applications of Artificial Intelligence
language:en
Short-container-title:Engineering Applications of Artificial Intelligence

Author:

Echevarría Yuviny,Blanco Cecilio,Sánchez Luciano

Funder

Eureka SD project, Spain

EU’s Erasmus Mundus programme, Spain

Spanish Ministry of Science and Innovation

Government of the Principality of Asturias, Spain

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Artificial Intelligence,Control and Systems Engineering

Reference57 articles.

1. Fast charging technique for high power LiFePO 4 batteries: A mechanistic analysis of aging;Anseán;J. Power Sources,2016

2. Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework;Bader-El-Den;Memet. Comput.,2009

3. Many-objective optimization using evolutionary algorithms: A survey;Bechikh,2017

4. Evolving diverse ensembles using genetic programming for classification with unbalanced data;Bhowan;IEEE Trans. Evol. Comput.,2013

5. An equivalent circuit model with variable effective Capacity for LiFePO4 batteries;Blanco;IEEE Trans. Veh. Technol.,2014

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1. Li-ion battery state of health prediction through metaheuristic algorithms and genetic programming;Energy Reports;2024-12

2. Robust battery lifetime prediction with noisy measurements via total-least-squares regression;Integration;2024-05

3. Prognostics of lithium-ion batteries health state based on adaptive mode decomposition and long short-term memory neural network;Engineering Applications of Artificial Intelligence;2024-01

4. Screening of retired batteries with gramian angular difference fields and ConvNeXt;Engineering Applications of Artificial Intelligence;2023-08

5. Using Genetic Programming to Learn Behavioral Models of Lithium Batteries;Applications of Evolutionary Computation;2023

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