An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

Cheng, Yujie; Song, Dengwei; Wang, Zhenya; Lu, Chen; Zerhouni, Noureddine

doi:10.1016/j.apenergy.2020.114817

Cited by 38 publications

(16 citation statements)

References 30 publications

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“…In this way, the ensemble could be re-weighted over time to capture the inherent strengths of each ensemble member during different periods of volatility or growth. Similar approaches have been used successfully to forecast battery capacities and CO 2 emissions [ 66 , 67 ]. Second, we chose our predictor set as these variables were interpretable, they covered the entire temporal period of our tick paralysis data and, crucially, they are all continuously updated and stored in secure databases.…”

Section: Discussionmentioning

confidence: 99%

Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model

et al. 2022

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Tick paralysis resulting from bites from Ixodes holocyclus and I. cornuatus is one of the leading causes of emergency veterinary admissions for companion animals in Australia, often resulting in death if left untreated. Availability of timely information on periods of increased risk can help modulate behaviors that reduce exposures to ticks and improve awareness of owners for the need of lifesaving preventative ectoparasite treatment. Improved awareness of clinicians and pet owners about temporal changes in tick paralysis risk can be assisted by ecological forecasting frameworks that integrate environmental information into statistical time series models. Using an 11-year time series of tick paralysis cases from veterinary clinics in one of Australia’s hotspots for the paralysis tick Ixodes holocyclus, we asked whether an ensemble model could accurately forecast clinical caseloads over near-term horizons. We fit a series of statistical time series (ARIMA, GARCH) and generative models (Prophet, Generalised Additive Model) using environmental variables as predictors, and then combined forecasts into a weighted ensemble to minimise prediction interval error. Our results indicate that variables related to temperature anomalies, levels of vegetation moisture and the Southern Oscillation Index can be useful for predicting tick paralysis admissions. Our model forecasted tick paralysis cases with exceptional accuracy while preserving epidemiological interpretability, outperforming a field-leading benchmark Exponential Smoothing model by reducing both point and prediction interval errors. Using online particle filtering to assimilate new observations and adjust forecast distributions when new data became available, our model adapted to changing temporal conditions and provided further reduced forecast errors. We expect our model pipeline to act as a platform for developing early warning systems that can notify clinicians and pet owners about heightened risks of environmentally driven veterinary conditions.

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Section: Discussionmentioning

confidence: 99%

Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model

et al. 2022

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“…introduces an induced ordered weighted averaging (IOWA) operator to attain time-varying weight allocation of RBF NN, gray model (GM), autoregressive integrated moving average (ARIMA), and SVM. By summing the weighted prognostic results of each member, the integrated estimation of battery capacity is finally achieved ( Cheng et al., 2020 ).…”

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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“…The proposed method provides the possibility of obtaining reliable information about the current RUL/SOH without needing to understand the basic physical processes that occur in LIB. Cheng et al (2020) proposed an inductive ordered weighted average (iowa) operator based on verification data, which realized the weight distribution that changes over time, that is, the V-IOWA operator. By summing the weighted prediction results of each member prediction algorithm, the overall prediction result is finally obtained.…”

Section: Othersmentioning

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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An ensemble prognostic method for lithium-ion battery capacity estimation based on time-varying weight allocation

Cited by 38 publications

References 30 publications

Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model

Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model

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

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

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