Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

HAVANGI RAMAZAN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING) Year:2021 | Volume:12 | Issue:1 Page(s): 61-73

Download Full-Text

Persian Verion

View:

358

Download:

Cites:

Information Journal Paper

Title

Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter

Author(s)

HAVANGI RAMAZAN | Issue Writer Certificate

Keywords

Lithium-ion Battery

State of Charge Estimation

Particle Filter

Abstract

Estimating the status of battery charge (SOC) in lithium-ion batteries is important not only for optimum energy management but also for ensuring safe operation and preventing charge and discharge and thus reducing battery life. However, this parameter cannot be directly measured from the battery terminals. Therefore, SOC needs to be estimated. In this paper, the recursive least squares method (RLS) is used to estimate the battery parameters and the modified Particle Filter is used to estimate the SOC of lithium-ion batteries. The standard Particle Filter has the problem of particle degeneracy phenomenon, which reduces estimation accuracy. Therefore, in modified Particle Filter, the difference evolutionary algorithm and the Markov chain Monte Carlo) MCMC (method are applied to the standard PF, that makes the estimation of SOC more accurate and consistent. In order to evaluate the performance of the proposed method, this method is compared with the classical methods. The results show the effective performance of the proposed method compared to other methods.

Cites

No record.

References

No record.

Cite

APA: Copy

HAVANGI, RAMAZAN. (2021). Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), 12(1 ), 61-73. SID. https://sid.ir/paper/958538/en

Vancouver: Copy

HAVANGI RAMAZAN. Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter. COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING)[Internet]. 2021;12(1 ):61-73. Available from: https://sid.ir/paper/958538/en

IEEE: Copy

RAMAZAN HAVANGI, “Simultaneous Estimation of model parameters and state-of-charge of Lithium-Ion Batteries using Recursive least squares and Modified Particle Filter,” COMPUTATIONAL INTELLIGENCE IN ELECTRICAL ENGINEERING (INTELLIGENT SYSTEMS IN ELECTRICAL ENGINEERING), vol. 12, no. 1 , pp. 61–73, 2021, [Online]. Available: https://sid.ir/paper/958538/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops