Piecewise Model-Based Online Prognosis of Lithium-Ion Batteries Using Particle Filters

Pugalenthi, Karkulali; Park, Hyun-Seok; Raghavan, Nagarajan

doi:10.1109/access.2020.3017810

Cited by 18 publications

(9 citation statements)

References 30 publications

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“…It dissected the internal ageing mechanism and degradation characteristics from the physical principal level of the lithium battery. In [18]- [20], the degradation effect of lithium batteries under different operating conditions was investigated. They measured the degradation data at different DOD, operating temperatures, and charge/discharge current rates and obtained the degradation models for lithium batteries.…”

Section: A Literature Reviewmentioning

confidence: 99%

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Wang

et al. 2023

IEEE Access

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The nonlinear degradation effect of the fast lithium battery energy storage system (FLBESS) and the time-scale variability of peak shaving and frequency regulation both make the optimal operation of the microgrid more challenging. Therefore, this paper presents a bi-level optimization model to balance the degradation cost of lithium battery and the benefit of peak shaving and frequency regulation and improve the utilization of FLBESS while dealing with the multiple time scales optimization problems. The upper-level model mainly optimizes the day-ahead hourly power to minimize the power purchase cost and wind and solar power curtailment cost while calculating the internal parameters of the FLBESS to supply the lower-level model for the solution. The lower model optimizes the intra-hour economic cost and coordinates the frequency regulation benefit and lithium battery degradation cost. Finally, case studies are conducted using a regional microgrid to verify that the model solution satisfies the requirements for optimal system operation. In contrast, the model has the advantages of lower economic cost, more significant life extension effect, and higher energy storage utilization rate. The results show that the annual costs were reduced by 14.9%, and the battery life was increased by 19.0% than without considering degradation.INDEX TERMS Fast lithium battery energy storage system, bi-level optimization, nonlinear degradation effect, frequency regulation, multiple time scales. NOMENCLATURE A. ACRONYMS

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Section: A Literature Reviewmentioning

confidence: 99%

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Wang

et al. 2023

IEEE Access

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“…This hybrid method can obtain prediction uncertainty while predicting the degradation process. Pugalenthi et al (2020) proposed a piecewise model to capture the two-phase degradation trend including linear and exponential models. The proposed model is incorporated into the particle filter framework to predict the degradation trajectory of the battery.…”

Section: Particle Filtering Prediction Strategiesmentioning

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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“…In [19], a novel data-driven prognostic approach is proposed for the remaining useful life prediction of lithium-ion batteries, which is combined with the Verhulst model, particle swarm optimization and particle filter algorithm. In [20], a piecewise degradation model along with a novel methodology is proposed to determine the inflection point, which is incorporated into a particle filter framework to predict the remaining useful life of lithium-ion batteries. In [21], a novel approach of particle-filtering-based prognostics is proposed to estimate the state of charge and state of maximum power available of lithium-ion batteries in the context of electromobility applications.…”

Section: Introductionmentioning

confidence: 99%

Remaining Useful Life Prediction of Lithium-ion Batteries using Particle Filter Algorithm in Communication Power Supply

Wu¹,

Wang

Zhang

et al. 2023

Preprint

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With the development of communication technologies, the technology of power support, particularly the technology of lithium-ion batteries plays an important role in the communication area. The evaluation for the remaining useful life of lithium-ion batteries is a hot issue, to provide the maintenance plans and ensure the reliability and safety of system. In this paper, a method using particle filter algorithm for the remaining useful life prediction of lithium-ion batteries in communication power supply is carried out, and the comparisons of prediction effect with the least square method are investigated. From the results, it is clearly seen that the proposed particle filter algorithm presents a better prediction accuracy and stability than the least square method. In addition , the proposed particle filter algorithm presents a weak data dependence than the least square method. For a better prediction result, the number of data to be predicted by the proposed particle filter algorithm accounts about 55% of the total number of the cycle time, which is smaller than that accounts 70% by the least square method. This paper provides a technology support for the remaining useful life prediction of lithium-ion batteries.

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Piecewise Model-Based Online Prognosis of Lithium-Ion Batteries Using Particle Filters

Cited by 18 publications

References 30 publications

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

Bi-Level Optimizing Model for Microgrids With Fast Lithium Battery Energy Storage Considering Degradation Effect

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

Remaining Useful Life Prediction of Lithium-ion Batteries using Particle Filter Algorithm in Communication Power Supply

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