Lithium-ion batteries life prediction method basedon degenerative characters and improved particle filter

Fang, Hongzheng; Fan, Hongqi; Ma, Haodong; Shen, Hui; Dong, Yunfan

doi:10.1109/icphm.2015.7245051

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…Although all batteries handle the same range of operating temperatures, the Li-Ion batteries are those with better benefits than the rest. Among the main advantages are: higher average nominal output voltage, high energy density and power, low energy loss by self-discharge, no memory effect and longer service life [50] [51] [52]. This battery is used in a variety of applications such as power tools, medical equipment, EVs, satellites, etc.…”

Section: A Basic Theoretical Concepts In the Electrical Behavior Of mentioning

confidence: 99%

A Survey on Technologies to Implement Battery Emulators based on DC/DC Power Converters

Rubén¹,

Gonzalo²,

Pablo³

et al. 2016

Proceedings of the 14th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering Innovati

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Currently, many researchers from industry and academic institutions are working on the study of electric vehicles, battery systems and renewable energy sources (solar, wind, geothermal, etc.). In order to carry out research on each of these technologies it is required large capital investment for equipment. Additionally, problems must be faced such as variability of environmental parameters, large space requirements, pollution (in some cases), etc. The use of software and hardware to emulate different types of actual batteries shows to be a low-cost and environmentally friendly option due to some of its features. The battery emulator system has the ability to replicate the behavior of any kind of actual battery using a virtual battery model (also known as battery electric model) and a controllable DC/DC power converter to be used in laboratory environments. Moreover, emulators are not only used for batteries, these can be used to emulate fuel cells, photovoltaic systems, wind generators, thermoelectric generator, etc. This survey aims to present the technologies to implement battery emulators based on DC/DC power converters. This survey is split up into five sections. In the first section, as an introduction, we present the advantages offered by the use of battery emulators to the scientific community and the environment. The second section presents a review about batteries (technologies, models, model parameters, etc.) and also it includes the virtual battery models, which are the main components of battery emulators based on DC/DC power converters. In the third section, we present the main DC/DC power converter types used in these systems and their operation modes. Additionally, in section 4 are presented some case studies where different topologies battery emulators based on DC/DC power converters with battery model are analyzed. These topologies are focused on their use in electric vehicles (EV).Digital Object Identifier (DOI):http://dx. first section, as an introduction, we present the advantages offered by the use of battery emulators to the scientific community and the environment. The second section presents a review about batteries (technologies, models, model parameters, etc.) Abstract-Currently, many researchers from industry and academic institutions are working on the study of electric vehicles, battery systems and renewable energy sources (solar, wind, geothermal, etc.). In order to carry out research on each of these technologies it is required large capital investment for equipment. Additionally, problems must be faced such as variability of environmental parameters, large space requirements, pollution (in some cases), etc. The use of software and hardware to emulate different types of actual batteries shows to be a low-cost and environmentally friendly option due to some of its features. The battery emulator system has the ability to replicate the behavior of any kind of actual battery using a virtual battery model (also known as battery electric model) and a controllable DC/DC power converte...

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Section: A Basic Theoretical Concepts In the Electrical Behavior Of mentioning

confidence: 99%

A Survey on Technologies to Implement Battery Emulators based on DC/DC Power Converters

Rubén¹,

Gonzalo²,

Pablo³

et al. 2016

Proceedings of the 14th LACCEI International Multi-Conference for Engineering, Education, and Technology: “Engineering Innovati

View full text Add to dashboard Cite

show abstract

“…Because of their considerable advantages over battery technologies, lithium‐ion (Li‐ion) batteries dominate the battery market. Their main advantages are high nominal cell voltage, high energy density and power, weak self‐discharge rate, no memory impact, and longer lifespan 6–8 …”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification strategy applied to lithium‐ion battery model

et al. 2021

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Summary This paper presents an optimal parameter identification strategy of the lithium‐ion (Li‐ion) battery model applying a recent metaheuristic artificial ecosystem‐based optimization (AEO) algorithm, which proves its ability in terms of both convergence speed and complexity. The key idea is to update the battery model parameters using the optimizer outputs. In the current paper, the battery model is based on the Shepherd model. To demonstrate the superiority of the suggested method of identification, the test results are compared in terms of efficiency, convergence speed, and accuracy of identification with those obtained by the salp swarm algorithm, the political optimizer, the equilibrium optimizer, and particle swarm optimization. Through the optimization procedure, the undetermined parameters of the battery model are employed as decision variables, but the root‐mean‐square error between estimated data and battery data is assigned to be an objective function must be minimal. The results showed the superior identification ability of the AEO compared to the other optimizers. This optimizer achieved 99.9% identification efficiency, which makes it an ideal solution for battery identification. Besides its identification efficiency, the AEO is much faster than the other optimizers, as the results show.

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