Lithium-ion batteries remaining useful life prediction based on BLS-RVM

Chen, Zewang; Shi, Na; Ji, Yufan; Niu, Mu; Wang, Youren

doi:10.1016/j.energy.2021.121269

Cited by 109 publications

(38 citation statements)

References 34 publications

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“…Assuming that the original signal sequence and Gaussian white noise are s(n) and ](n), respectively, and the i-th composite signal after the addition of Gaussian white noise can be represented as s i (n) s(n) + ] i (n). The detailed steps are given as follows (Chen Z. et al, 2021;Peng et al, 2021):…”

Section: Figurementioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

et al. 2022

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The remaining useful life (RUL) prediction of Lithium-ion batteries (LIBs) is a crucial element of battery health management. The accurate prediction of RUL enables the maintenance and replacement of batteries with potential safety hazards, which ensures safe and stable battery operation. This paper develops a new method for the RUL prediction of LIBs, which is combined with complete ensemble empirical mode decomposition with adaptive noise (CEEDMAN), whale optimization algorithm (WOA), and support vector regression (SVR). Firstly, the CEEMDAN is employed to perform noise reduction in battery capacity data for prediction accuracy improvement. Then, an SVR model optimized by the WOA is proposed to predict the RUL. Finally, the public battery datasets are selected to validate the performance of the CEEMDAN-WOA-SVR method. The RUL prediction accuracy of the CEEMDAN-WOA-SVR method is better than the WOA-SVR method. In addition, a comparison is made between the proposed method and the existing methods (artificial bee colony algorithm-SVR method, ensemble empirical mode decomposition-gray wolf optimization-SVR method). The results show that the accurate prediction of the proposed method is superior to the two methods.

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Section: Figurementioning

confidence: 99%

Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

et al. 2022

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“…Sınırlı kapasite, şarj/deşarj esnasında eletrotlarda hacimsel artıştan kaynaklanan patlama riski ise en belirgin dezavantajıdır. (Makuza, et al, 2021) (Chen, et al, 2021).…”

Section: şEkil 5 Lityum-iyon Pil şEmasıunclassified

Geçmişten Günümüze Batarya Teknolojisi

Efe¹,

GÜNGÖR²

2022

European Journal of Science and Technology

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Özİnsanoğlunun yaşamını idame edebilmesi için enerjiye olan bağımlılığı, her geçen gün artan enerji talebini oluşturmuş ve bunun sonucu olarak da dünya üzerinde en çok araştırma yapılan konuların başına enerjiyi taşımıştır. Günümüzde gelişen elektronik ve nano teknolojisinin ürünleri olan robotik, bilgisayar, cep telefonu, elektrikli araçlar, uzay sistemleri gibi teknolojik sistemler; yüksek enerji potansiyeline sahip, hafif, kolay ulaşılabilir, ucuz ve güvenilir enerji kaynağına ihtiyaç duymaktadır. Hareketli ve taşınabilir olan bu sistemlerde ihtiyaç duyulan enerjinin sürekliliği ve çevreyle dost olması kadar depolana bilirliği de önemli bir husustur. Günümüze kadar yürütülen enerji depolama çalışmalarında, depolanacak enerjinin çeşidine bağlı olarak; ısıl enerjiyi, elektrik enerjisini, mekanik enerjiyi ve kimyasal enerjiyi depolama şeklinde birçok farklı metot denenmiştir. Bu çalışmada, teknolojik sistemlerde kullanımı yaygın bir şekilde tercih edilen, enerjinin kimyasal olarak depolandığı batarya teknolojisi ele alınmıştır. Çalışmada geçmişten günümüze kadar batarya çeşitleri, çalışma prensipleri, avantajları, dezavantajları karşılaştırmalı olarak incelenmiştir.

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“…Cheng et al proposed an optimization algorithm based on a combination of the broad learning system (BLS) and relevance vector machine (RVM), which inputs different features into the BLS network, sets different prediction starting points, and finally outputs the RUL prediction of the hybrid model. This algorithm improves the shortcomings of the original RVM algorithm and has stronger long-term prediction ability and generalization ability [ 21 ]. When introducing the above data-driven method, the deep-learning method is also excellent.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

Wang

Wen

2022

Sensors

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Lithium-ion batteries have become an important power source in low-carbon transportation energy, and the safe operation and remaining useful life prediction are of great significance. Aiming at the shortcomings of existing methods, such as low prediction accuracy and a short prediction period, this paper proposes a real-time update high-order extended Kalman filter method based on fixed-step life prediction for vehicle lithium batteries based on the principle of combining models and data. First, the state model describing the parameters in the dynamic energy attenuation model is established, and the energy attenuation model is regarded as the observation model of the system to meet the requirements of establishing the Kalman filter. Secondly, the multi-step prediction equation of the state model is established by iterative recursion. At the same time, the multi-step prediction equation between the existing energy output value and the future output value is established based on the multi-dimensional Taylor network (MTN). The multiplicative noise term introduced in the dynamic modeling process is regarded as the hidden variable of the system to meet the requirements of establishing the multi-step linear predictive Kalman filter. Finally, the effectiveness of the new method is verified by digital simulation examples.

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Lithium-ion batteries remaining useful life prediction based on BLS-RVM

Cited by 109 publications

References 34 publications

Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

Remaining useful life prediction of lithium-ion batteries using CEEMDAN and WOA-SVR model

Geçmişten Günümüze Batarya Teknolojisi

Real-Time Updating High-Order Extended Kalman Filtering Method Based on Fixed-Step Life Prediction for Vehicle Lithium-Ion Batteries

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