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The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Journal of Energy Chemistry 2023年第7期82卷 103-121,I0003页

作者： Xingjun Li Dan Yu Vilsen Søren Byg Store Daniel Ioan Department of Energy Aalborg UniversityAalborg 9220Denmark Department of Mathematical Sciences Aalborg UniversityAalborg 9220Denmark

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident prevention.This work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions.The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper.The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers.Then the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are presented.The research core of common ML algorithms is given first time in a uniform format in chronological order.The algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further outlooked.Finally,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is outlooked.Battery lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the future.This paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

关键词： Lithium-ion batteries Remaining useful lifetime prediction Machine learning lifetime extension

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An Energy-Efficient Protocol Using an Objective Function & Random Search with Jumps forWSN

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Computers, Materials & Continua 2019年第3期58卷 603-624页

作者： Mohammed Kaddi Khelifa Benahmed Mohammed Omari Faculty of Technology University Tahri MohammedBechar08000Algeria Faculty of Exact Sciences University Tahri MohammedBechar08000Algeria LDDI Laboratory Math and CS DepartmentUniversity of AdrarAdrar01000Algeria.

Wireless Sensor Networks(WSNs)have hardware and software limitations and are deployed in hostile environments.The problem of energy consumption in WSNs has become a very important axis of research.To obtain good performance in terms of the network lifetime,several routing protocols have been proposed in the literature.Hierarchical routing is considered to be the most favorable approach in terms of energy efficiency.It is based on the concept parent-child hierarchy where the child nodes forward their messages to their parent,and then the parent node forwards them,directly or via other parent nodes,to the base station(sink).In this paper,we present a new Energy-Efficient clustering protocol for WSNs using an Objective Function and Random Search with Jumps(EEOFRSJ)in order to reduce sensor energy consumption.First,the objective function is used to find an optimal cluster formation taking into account the ratio of the mean Euclidean distance of the nodes to their associated cluster heads(CH)and their residual energy.Then,we find the best path to transmit data from the CHs nodes to the base station(BS)using a random search with jumps.We simulated our proposed approach compared with the Energy-Efficient in WSNs using Fuzzy C-Means clustering(EEFCM)protocol using Matlab Simulink.Simulation results have shown that our proposed protocol excels regarding energy consumption,resulting in network lifetime extension.

关键词： WSNs clustering energy consumption lifetime extension random search with jumps EEOFRSJ EEFCM.

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