Journal ArticleDOI
An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system
TLDR
A novel ensemble learning framework to estimate the battery SOH, which can boost the performance of the data driven SOH estimation through a well-designed integration of the weak learners is proposed.About:
This article is published in Energy.The article was published on 2020-09-01. It has received 62 citations till now. The article focuses on the topics: State of health & Battery (electricity).read more
Citations
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Journal ArticleDOI
Battery energy-storage system: A review of technologies, optimization objectives, constraints, approaches, and outstanding issues
Mahammad A. Hannan,Safat Bin Wali,Pin Jern Ker,M. S. Abd Rahman,Mohd Helmi Mansor,Vigna K. Ramachandaramurthy,Kashem M. Muttaqi,Teuku Meurah Indra Mahlia,Zhao Yang Dong +8 more
TL;DR: A comprehensive review of the battery energy-storage system concerning optimal sizing objectives, the system constraint, various optimization models, and approaches along with their advantages and weakness is provided.
Journal ArticleDOI
A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
TL;DR: The five most studied types of ML algorithms for battery SOH estimation are systematically reviewed and it can be concluded that amongst these methods, support vector machine and artificial neural network algorithms are still research hotspots.
Journal ArticleDOI
A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles
TL;DR: A review of the state-of-the-art online SOC and SOH evaluation technologies published within the recent five years in view of their advantages and limitations and suggests future work in the real-time battery management technology.
Journal ArticleDOI
Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network
Gong Cheng,Xinzhi Wang,Yurong He +2 more
TL;DR: This work combined the empirical mode decomposition (EMD) method and backpropagation long-short-term memory (B-LSTM) neural network (NN) to develop SOH estimation and RUL prediction models that have high robustness, good accuracy, and applicability.
Journal ArticleDOI
Prognostics of battery cycle life in the early-cycle stage based on hybrid model
TL;DR: A hybrid prediction model, which integrates random forest, Artificial Bee Colony and general regression neural network, called RF-ABC-GRNN is proposed, which could effectively screen out the high-importance features and make accurate prediction much earlier.
References
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Journal ArticleDOI
Ageing mechanisms in lithium-ion batteries
Jens Vetter,Petr Novák,Markus Robert Wagner,Claudia Veit,Kai-Christian Möller,Jürgen Besenhard,Martin Winter,Margret Wohlfahrt-Mehrens,C Vogler,Abderrezak Hammouche +9 more
TL;DR: In this article, the mechanisms of lithium-ion battery ageing are reviewed and evaluated, and the most promising candidate as the power source for (hybrid) electric vehicles and stationary energy storage.
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Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization
TL;DR: This paper proposes a self- Adaptive DE (SaDE) algorithm, in which both trial vector generation strategies and their associated control parameter values are gradually self-adapted by learning from their previous experiences in generating promising solutions.
Book
Ensemble Methods: Foundations and Algorithms
TL;DR: An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks and gives the necessary groundwork to carry out further research in this evolving field.
Journal ArticleDOI
Translation techniques in cross-language information retrieval
TL;DR: Over the last 15 years, the CLIR community has developed a wide range of techniques and models supporting free text translation, with a special emphasis on recent developments.
Journal ArticleDOI
Energy storage deployment and innovation for the clean energy transition
TL;DR: In this article, a two-factor model that integrates the value of investment in materials innovation and technology deployment over time from an empirical dataset covering battery storage technology is presented, and a viable path to dispatchable US$1W−1 solar with US$100kWh−1 battery storage is charted.