Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

Yu, Jing; Yang, Jie; Wu, Yao; Tang, Diyin; Dai, Jing

doi:10.1002/er.5750

Cited by 24 publications

(9 citation statements)

References 50 publications

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“…40 However, since conventional sensors cannot directly obtain SOH, it is still difficult to make an online estimation of the SOH. In the literature, 41 a semi-supervised learning framework was implemented to estimate the ability of unlabeled data to obtain better prediction performance. The local linear reconstruction method was adopted to determine the capacity distribution of unlabeled data, and the support vector regression model was proposed for predicting the remaining useful life of the battery.…”

Section: Soh Equalizationmentioning

confidence: 99%

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

2021

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Series-connected lithium battery packs are widely adopted in industries such as electrical vehicles and large-scale energy storage systems. It is necessary to configure an equalization system for them to reduce the inconsistency of single cells, to ensure the battery pack cycle capacity. Although many novel active converters have been proposed for equalization, there still lacks systematic analyses on variables, topologies, and modular methods. Therefore, this paper provides a systematic review on the above aspects. Initially, an overall summary is present on current variables for equalization control. Second, the dominant structures are divided into cell-to-cell, cell-to-pack, and pack-to-pack topologies, and their characteristics are pointed out for comparison. And then, the modular methods, including single-layer-based, multi-layer-based, and hybrid integrations are evaluated via graph theory. Some key issues and potential research directions are given, which are expected to offer assistance on the selection of appropriate variables and topologies for equalization systems in the future.

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Section: Soh Equalizationmentioning

confidence: 99%

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

2021

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“…If available at all, capacities will only be available for limited cycles. This raises the need for semi-supervised learning, as addressed in (Yu, Yang, Wu, Tang, & Dai, 2020).…”

Section: Regression Type Modelsmentioning

confidence: 99%

“…One aspect of missing data is that data streams will typically not contain capacity or SOH for all data points. Hence, models that can be applied with no or limited labelled data may be needed, indicating that methods from unsupervised or semisupervised learning could be relevant (Yu et al, 2020).…”

Section: Statistical and Machine Learning Modelsmentioning

confidence: 99%

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Vanem¹,

Alnes

Lam³

2021

PHM_CONF

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Battery systems are becoming an increasingly attractive alternative for powering ocean going ships, and the number of fully electric or hybrid ships relying on battery power for propulsion and maneuvering is growing. In order to ensure the safety of such electric ships, it is of paramount importance to monitor the available energy that can be stored in the batteries, and classification societies typically require that the state of health of the batteries can be verified by independent tests – annual capacity tests. However, this paper discusses data-driven diagnostics for state of health modelling for maritime battery systems based on operational sensor data collected from the batteries as an alternative approach. Thus, this paper presents a comprehensive review of different data-driven approaches to state of health modelling, and aims at giving an overview of current state of the art. Furthermore, the various methods for data-driven diagnostics are categorized in a few overall approaches with quite different properties and requirements with respect to data for training and from the operational phase. More than 300 papers have been reviewed, most of which are referred to in this paper. Moreover, some reflections and discussions on what types of approaches can be suitable for modelling and independent verification of state of health for maritime battery systems are presented.

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“…Other approaches have split their linear functions according to voltage or SoC regions [33], [38], [39]. It has even been possible to use separate linear models according to how a cell is being used [40] or to use locally linear regression, where linear models are constructed based on a number of nearest neighbours, to predict capacity loss [41].…”

Section: Introductionmentioning

confidence: 99%

Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis

Greenbank,

Howey

2021

Preprint

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The complex nature of lithium-ion battery degradation has led to many machine learning based approaches to health forecasting being proposed in literature. However, machine learning can be computationally intensive. Linear approaches are faster but have previously been too inflexible for successful prognosis. For both techniques, the choice and quality of the inputs is a limiting factor of performance. Piecewise-linear models, combined with automated feature selection, offer a fast and flexible alternative without being as computationally intensive as machine learning. Here, a piecewise-linear approach to battery health forecasting was compared to a Gaussian process regression tool and found to perform equally well. The input feature selection process demonstrated the benefit of limiting the correlation between inputs. Further trials found that the piecewise-linear approach was robust to changing input size and availability of training data.

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Online state‐of‐health prediction of lithium‐ion batteries with limited labeled data

Abstract: State-of-health (SOH) plays a vital role in battery health management and power system stability. This process can be achieved by capacity estimation.

Cited by 24 publications

References 50 publications

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

Piecewise-linear modelling with feature selection for Li-ion battery end of life prognosis

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