A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve

Guo, Yongfang; Huang, Kai; Hu, Xiaoya

doi:10.1016/j.est.2021.102372

Cited by 59 publications

(19 citation statements)

References 36 publications

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“…The LS-SVM algorithm is a typical classification and regression method, and it features the advantages of acceptable accuracy in high dimensional systems and quick computation capability. Other classification and regression methods, such as relevance vector machine ( Guo et al., 2021 ) and random forest ( Mawonou et al., 2021 ), can also be leveraged to estimate battery SoH. Moreover, this protocol can also be extended to other machine learning algorithms such as neural networks (including Elman neural network ( Li et al., 2019 ), extreme learning machine ( Chen et al., 2021 ), long-short term memory recurrent neural network ( Li et al., 2020 ), gated recurrent unit based neural network ( Chen et al., 2022 )).…”

Section: Expected Outcomesmentioning

confidence: 99%

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Shu

Shen

et al. 2022

STAR Protocols

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Section: Expected Outcomesmentioning

confidence: 99%

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Shu

Shen

et al. 2022

STAR Protocols

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“…In another example, Guo et al. reconstructs the battery terminal voltage profiles based on a linear interpolation method to eliminate the noise, and avoid extraction of wrong health features from IC curve, then an SVR model is built to map the health feature and the battery SOH ( Guo et al., 2021b ). To alleviate the problem of intensive calculation when facing with a large amount of sampling data, the least squares SVM (LSSVM) is advanced to convert the quadratic programming problem into a linear programming problem.…”

Section: Machine-learning-based Soh Predictionmentioning

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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“…SOH is usually defined as a ratio of battery capacity in the current charging/discharging cycle to its nominal capacity. [5] SOH ¼…”

Section: Introductionmentioning

confidence: 99%

State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy

Guo

Wang

et al. 2022

Energy Tech

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Accurate prediction of the state of health (SOH) is essential to ensure the safety and reliability of battery operation. The thermal factor is an important indicator of SOH and many methods based on temperature are sensitive to measurement noise. To improve SOH estimation precision, a new health indicator (HI) directly extracted from the temperature curve is developed and an integrated multi-Gaussian process regression (MGPR) model is proposed. First, based on the trend analysis of the charging temperature curves with battery degradation, three features that can reflect thermal characteristics are extracted as the HI. Second, considering that the model generated by machine learning is influenced by the training dataset and the inherent inconsistencies in batteries, MGPR model is proposed to improve the model fitness. The training data is reformed and multiple GPR models are established. The multiple models are weighed by taking into account the prediction uncertainty to get the final SOH estimation result. Finally, two types of open-source data relative to different ambient temperatures and operating profiles are used to verify the performance. Experiment results show that the HI developed can characterize the battery degradation well and the MGPR model has high robustness and can obtain high-precision estimation results.

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A state-of-health estimation method of lithium-ion batteries based on multi-feature extracted from constant current charging curve

Cited by 59 publications

References 36 publications

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

Protocol for state-of-health prediction of lithium-ion batteries based on machine learning

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

State‐of‐Health Estimation of Lithium‐Ion Batteries Based on Thermal Characteristics Mining and Multi‐Gaussian Process Regression Strategy

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