Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Tian, Jinpeng; Xiong, Rui; Shen, Weixiang; Lu, Jiahuan

doi:10.1002/eom2.12213

Cited by 20 publications

(7 citation statements)

References 60 publications

(119 reference statements)

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“…For instance, many feature-based models are presented in [14]; one, in particular, is a one-cycle one (from non-longitudinal features), but such a model is not discussed. Two recent distinguishable contributions within the one-cycle context, and each with its distinct flavour, are: [34] using impedance-based forecasting and taking as input the future cycling protocol and a single electrochemical impedance spectroscopy measurement; and the other using a sequence-to-sequence (seq2seq) model to predict voltage-capacity curves (so many cycles) ahead of time [35].…”

Section: Introductionmentioning

confidence: 99%

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

2023

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Lithium-ion batteries have found applications in many parts of our daily lives. Predicting their remaining useful life (RUL) is thus essential for management and prognostics. Most approaches look at early life prediction of RUL in the context of designing charging profiles or optimising cell design. While critical, said approaches are not directly applicable to the regular testing of cells used in applications. This article focuses on a class of models called ‘one-cycle’ models which are suitable for this task and characterized by versatility (in terms of online prediction frameworks and model combinations), prediction from limited input, and cells’ history independence. Our contribution is fourfold. First, we show the wider deployability of the so-called one-cycle model for a different type of battery data, thus confirming its wider scope of use. Second, reflecting on how prediction models can be leveraged within battery management cloud solutions, we propose a universal Exponential-smoothing (e-forgetting) mechanism that leverages cycle-to-cycle prediction updates to reduce prediction variance. Third, we use this new model as a second-life assessment tool by proposing a knee region classifier. Last, using model ensembling, we build a “model of models”. We show that it outperforms each underpinning model (from in-cycle variability, cycle-to-cycle variability, and empirical models). This ‘ensembling’ strategy allows coupling explainable and black-box methods, thus giving the user extra control over the final model.

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Section: Introductionmentioning

confidence: 99%

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

2023

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“…As an important branch of machine learning, deep learning (DL) models have received extensive attention due to the well performance in FDD applications of industrial systems in recent years. In addition, the validity and generality of DL models in the field of battery health management have been demonstrated, such as capacity estimation and remaining useful life prediction for lithium-ion batteries (Tian et al 2022;Ma et al 2022;Tian et al 2021). DL models directly learn hidden high-dimensional features from original signals through multi-layer data processing units, overcoming the dependence of model training on subjective prior information.…”

Section: Introductionmentioning

confidence: 99%

Recent deep learning models for diagnosis and health monitoring: A review of research works and future challenges

Lou

Atoui

2023

Transactions of the Institute of Measurement and Control

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As an important branch of machine learning, deep learning (DL) models with multiple hidden layer structures have the ability to extract highly representative features from the input. At present, fault detection and diagnosis (FDD) and health monitoring solutions developed based on DL models have received extensive attention in academia and industry along with the rapid improvement of computing power. Therefore, this paper focuses on a comprehensive review of DL model–based FDD and health monitoring schemes in view of common problems of industrial systems. First, brief theoretical backgrounds of basic DL models are introduced. Then, related publications are discussed about the development of DL and graphical models in the industrial context. Afterwards, public data sets are summarized, which are associated with several research papers. More importantly, suggestions on DL model–based diagnosis and health monitoring solutions and future developments are given. Our work will have a positive impact on the selection and design of FDD solutions based on DL and graphical models in the future.

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“…Compared to model-based approaches, it does not require any complex physical model; it only builds a weight vector based upon its training data. Tian et al [ 25 ] proposed a deep learning sequence to sequence model to predict the capacity degradation of the Li-ion battery. The authors used the data of one cycle of the Li-ion battery for multistep (100, 200, and 300 cycles) ahead prediction.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

Ali

Zafar

Masood

et al. 2022

Computational Intelligence and Neuroscience

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In this paper, a novel multistep ahead predictor based upon a fusion of kernel recursive least square (KRLS) and Gaussian process regression (GPR) is proposed for the accurate prediction of the state of health (SoH) and remaining useful life (RUL) of lithium-ion batteries. The empirical mode decomposition is utilized to divide the battery capacity into local regeneration (intrinsic mode functions) and global degradation (residual). The KRLS and GPR submodels are employed to track the residual and intrinsic mode functions. For RUL, the KRLS predicted residual signal is utilized. The online available experimental battery aging data are used for the evaluation of the proposed model. The comparison analysis with other methodologies (i.e., GPR, KRLS, empirical mode decomposition with GPR, and empirical mode decomposition with KRLS) reveals the distinctiveness and superiority of the proposed approach. For 1-step ahead prediction, the proposed method tracks the trajectory with the root mean square error (RMSE) of 0.2299, and the increase of only 0.2243 RMSE is noted for 30-step ahead prediction. The RUL prediction using residual signal shows an increase of 3 to 5% in accuracy. This proposed methodology is a prospective approach for an efficient battery health prognostic.

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Data‐driven battery degradation prediction: Forecasting voltage‐capacity curves using one‐cycle data

Cited by 20 publications

References 60 publications

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Online Lifetime Prediction for Lithium-Ion Batteries with Cycle-by-Cycle Updates, Variance Reduction, and Model Ensembling

Recent deep learning models for diagnosis and health monitoring: A review of research works and future challenges

A Hybrid Data-Driven Approach for Multistep Ahead Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries

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