Parameter estimation for equivalent electrical model of lithium-ion cell

Dziechciaruk, Grzegorz; Ufnalski, Bartłomiej; Grzesiak, Lech M.

doi:10.23919/epe17ecceeurope.2017.8099328

Cited by 4 publications

(1 citation statement)

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“…Other works also chose GAs for the electrical models parameterization. 16,[21][22][23][24] Different AI methods are used to estimate battery model parameters, such as PSO, [25][26][27][28] and the simulated annealing method. 29 The upside of these methods is the fact that they escape from local minima, while their downside is, they have a processing time from a few minutes to a few hours, which makes it impossible to operate them in runtime on the device.…”

Section: Introductionmentioning

confidence: 99%

Multi‐phase method of estimation and adaptation of parameters of electrical battery models

Binelo

Sausen

et al. 2020

Int J Energy Res

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Mathematical modeling of the battery lifetime is an important tool for the design of more efficient batteries, as well as for the optimization of their use. The electrical models class is among the classes of mathematical models used for this purpose, and a fundamental step to their application is the correct estimation of their parameters. This paper performs the mathematical modeling of Lithium-Ion Polymer batteries lifetime through the electrical model of Tremblay, in which a multi-phase method of estimation and adaptation of parameters is proposed, divided into three phases: discovery, learning, and inference. The multi-phase method is based on two Artificial Intelligence techniques: genetic algorithms and artificial neural networks. The proposed method is validated by the simulation and experimental studies. From the results, it is concluded that the application of the multi-phase method improves the effective accuracy of the Tremblay model, when it comes to adapt its parameters to the battery during runtime. For constant discharge currents, the average error reduction was 79%, when compared to the best set of parameters obtained by GA without the adaptation process. For variable current discharge curves, the method was able to reduce the error more than 35%. This method can be applied to other battery lifetime prediction models.

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