Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model

Huang, Zhelin; Yang, Fangfang

doi:10.1155/2021/6618708

Cited by 2 publications

(1 citation statement)

References 24 publications

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“…The empirical model-based RUL prediction method builds the degradation model by fitting the historical degradation data of lithium ion battery with the empirical model, and updates the model parameters by the filtering method [75]. Finally, the battery life prediction is realized [76]. This model does not need to analyze the internal electrochemical reaction and has a wider application range [77].…”

Section: An Empirical Model-based Approachmentioning

confidence: 99%

Research Progress of Battery Life Prediction Methods Based on Physical Model

Wang

Liu

et al. 2023

Energies

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Remaining useful life prediction is of great significance for battery safety and maintenance. The remaining useful life prediction method, based on a physical model, has wide applicability and high prediction accuracy, which is the research hotspot of the next generation battery life prediction method. In this study, the prediction methods of battery life were compared and analyzed, and the prediction methods based on the physical model were summarized. The prediction methods were classified according to their different characteristics including the electrochemical model, equivalent circuit model, and empirical model. By analyzing the emphasis of electrochemical process simplification, different electrochemical models were classified including the P2D model, SP model, and electrochemical fusion model. The equivalent circuit model was divided into the Rint model, Thevenin model, PNGV model, and RC model for the change of electronic components in the model. According to the different mathematical expressions of constructing the empirical model, it can be divided into the exponential model, polynomial model, exponential and polynomial mixed model, and capacity degradation model. Through the collocation of different filtering methods, the different efficiency of the models is described in detail. The research progress of various prediction methods as well as the changes and characteristics of traditional models were compared and analyzed, and the future development of battery life prediction methods was prospected.

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Section: An Empirical Model-based Approachmentioning

confidence: 99%

Research Progress of Battery Life Prediction Methods Based on Physical Model

Wang

Liu

et al. 2023

Energies

View full text Add to dashboard Cite

show abstract

Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost

et al. 2022

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This work proposes a mixed-integer linear programming model for the operational cost function of lithium-ion batteries that should be applied in a microgrid centralized controller. Such a controller aims to supply loads while optimizing the leveled cost of energy, and for that, the cost function of the battery must compete with the cost functions of other energy resources, such as distribution network, dispatchable generators, and renewable sources. In this paper, in order to consider the battery lifetime degradation, the proposed operational cost model is based on the variation in its state of health (SOH). This variation is determined by experimental data that relate the number of charge and discharge cycles to some of the most important factors that degrade the lifespan of lithium-ion batteries, resulting in a simple empirical model that depends on the battery dispatch power and the current state of charge (SOC). As proof-of-concept, hardware-in-the-loop (HIL) simulations of a real microgrid are performed considering a centralized controller with the proposed battery degradation cost function model. The obtained results demonstrate that the proposed cost model properly maintains the charging/discharging rates and the SOC at adequate levels, avoiding accelerating the battery degradation with use. For the different scenarios analyzed, the battery is only dispatched to avoid excess demand charges and to absorb extra power produced by the non-dispatchable resources, while the daily average SOC ranges from 48.86% to 65.87% and the final SOC converges to a value close to 50%, regardless of the initial SOC considered.

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Quality Classification of Lithium Battery in Microgrid Networks Based on Smooth Localized Complex Exponential Model

Cited by 2 publications

References 24 publications

Research Progress of Battery Life Prediction Methods Based on Physical Model

Research Progress of Battery Life Prediction Methods Based on Physical Model

Mixed-Integer Linear Programming Model to Assess Lithium-Ion Battery Degradation Cost

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