Kalman filter Observer for SoC prediction of Lithium cells

Ayadi, Faten; Lahiani, Mongi; Derbel, Nabil

doi:10.25046/aj020424

Cited by 2 publications

(3 citation statements)

References 40 publications

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“…A stochastic fuzzy neural network based extended Kalman filter particularly utilized where the acceptable estimate for SOC is to be calculated. Whereas maximum error of SOC estimation is 0.6% when compared to real SOC upon discharge test [28].…”

Section: Kalman Filter (Kf)mentioning

confidence: 88%

Adaptive filter algorithms for state of charge estimation methods: A comprehensive review

Zulkifli

Khan

2022

IJEECS

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Battery <span>management system is compulsory for long life and effective utilization of lithium ion battery. State of charge (SOC) is key parameter of battery management system. SOC estimation isn’t an easy job. Effective estimation of SOC involves complex algorithms where. Conventional methods of SOC estimation does not take continuously varying battery parameters into account thus large noise in both voltage and current signal are observed resulting in inaccurate estimation of SOC. Therefore, in order to improve the accuracy and precision in SOC estimation, improved adaptive algorithms with better filtering are employed and discussed in this paper. These adaptive algorithms calculate time varying battery parameters and SOC estimation are performed while bringing both time scales into account. These time scales may be slow-varying characteristics or fast-varying characteristics of battery. Some experimentations papers have proved that these adaptive filter algorithms protect battery from severe degradation and accurately calculate battery SOC. This paper reviews all previously known adaptive filter algorithms, which is the future of the electrical vehicles. At the end, a comparison is built based upon recent papers which talked on SOC at their differences in control strategies, efficiency, effectiveness, reliability, computational time and cost</span>.

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Section: Kalman Filter (Kf)mentioning

confidence: 88%

Adaptive filter algorithms for state of charge estimation methods: A comprehensive review

Zulkifli

Khan

2022

IJEECS

View full text Add to dashboard Cite

show abstract

“…The total amount of lithium ions inside the cell remains constant thus an improvement one could make to the model would be to enforce this with an algebraic constraint. Some attempts at estimating the state of charge of a lithium-ion cell using a Kalman filter can be found here [2,3,6,57]. A DAE-compatible Kalman filter has been applied on a model of a lithium ion cell [12] with the goal of estimating the SOC.…”

Section: Chapter 5 Conclusionmentioning

confidence: 99%

“…Examples of a finite element simulation of the lithium-ion cell can be found here [63], finite difference [8], finite volume [77], spectral method [12]. Examples of applying a Kalman filter on a lithium-ion cell can be found here [3,6,12,17,34,49,51,57,64,71,75]. Other methods for estimating SOC include Monte Carlo [19] and neural networks [33].…”

Section: Chapter 1 Introductionmentioning

confidence: 99%