Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health

Sadabadi, Kaveh Khodadadi; Jin, Xin; Rizzoni, Giorgio

doi:10.1016/j.jpowsour.2020.228861

Cited by 78 publications

(31 citation statements)

References 20 publications

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“…Khodadadi Sadabadi et al. designs an enhanced SPM to depict the variation of parameters coupled with battery deterioration, and SOH is calibrated based on the number of mole cyclable lithium and the identified internal resistance ( Khodadadi Sadabadi et al., 2021 ).…”

Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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Summary Accurate state of health (SOH) prediction is significant to guarantee operation safety and avoid latent failures of lithium-ion batteries. With the development of communication and artificial intelligence technologies, a body of researches have been performed toward precise and reliable SOH prediction method based on machine learning (ML) techniques. In this paper, the conception of SOH is defined, and the state-of-the-art prediction methods are classified based on their primary implementation procedure. As an essential step in ML-based SOH algorithms, the health feature extraction methods reported in the literature are comprehensively surveyed. Next, an exhausted comparison is conducted to elaborate the development of ML-based SOH prediction techniques. Not only their advantages and disadvantages of the application in SOH prediction are reviewed but also their accuracy and execution process are fully discussed. Finally, pivotal challenges and corresponding research directions are provided for more reliable and high-fidelity SOH prediction.

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Section: Overview Of Soh Prediction Methodsmentioning

confidence: 99%

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Shu

Shen

et al. 2021

iScience

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“…Sadabadi et al estimated SOH of LIBs using the enhanced single particle model (eSPM). 198 In this study, the prediction response of SOH based on charging datasets is compared with ageing, in which cyclable lithium gets reduced due to the formation of a solid electrolyte interface (SEI) and plating within the cell. The number of moles of cyclable lithium and saturation concentration available in positive and negative cell electrodes is responsible for battery capacity degradation.…”

Section: Applications Of Model Based and Data Driven Techniques In Libsmentioning

confidence: 99%

Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches

Pandey

Parikh

Prochowicz

et al. 2023

Sustainable Energy Fuels

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“…Khodadadi Sadabadi et al (2021) developed a technique based on an electrochemical model that can estimate the SOH of LMO-NMC batteries using simulated in-situ vehicle data. The composite SOH metric derived from the estimated enhanced single particle model (eSPM) parameters is used to design the RUL predictor based on the PF.…”

Section: Particle Filtering Prediction Strategiesmentioning

confidence: 99%

“…Methods include model-based methods (such as physical models, electrochemical models, etc.) or data-driven methods (Guha and Patra, 2018a;Khodadadi Sadabadi et al, 2021;Lyu et al, 2021). Different types of aging models have been proposed in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

et al. 2021

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Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

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Prediction of remaining useful life for a composite electrode lithium ion battery cell using an electrochemical model to estimate the state of health

Cited by 78 publications

References 20 publications

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

State of health prediction of lithium-ion batteries based on machine learning: Advances and perspectives

Predicting the state parameters of lithium ion batteries: the race between filter-based and data driven approaches

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

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