An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries

Xingtao Liu, Chaoyi Zheng, Ji Wu, Jinhao Meng, Daniel-Ioan Stroe and Jiajia Chen
Additional contact information
Xingtao Liu: Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China
Chaoyi Zheng: Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China
Ji Wu: Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China
Jinhao Meng: Department of Electrical Engineering, Sichuan University, Chengdu 610065, China
Daniel-Ioan Stroe: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark
Jiajia Chen: Department of Vehicle Engineering, Hefei University of Technology, Hefei 230009, China

Energies, 2020, vol. 13, issue 2, 1-16

Abstract: In this paper, an improved method for estimating the state of charge (SOC) of lithium-ion batteries is proposed, which is developed from the particle filter (PF). An improved genetic particle filter (GPF), owing to the advantages of the PF and genetic algorithm, is proposed to overcome the disadvantage of the traditional particle filter: lacking the diversity of particles. Firstly, the relationship between SOC and open-circuit voltage (OCV) is identified on the low-current OCV test. Secondly, a first-order resistor and capacitance (RC) model is established, then, the least-squares algorithm is used to identify the model parameters via the incremental current test. Thirdly, GPF and the improved GPF (IGPF) are proposed to solve the problems of the PF. The method based on the IGPF is proposed to estimate the state of power (SOP). Finally, IGPF, GPF, and PF are employed to estimate the SOC on the federal urban driving schedule (FUDS). The results show that compared with traditional PF, the errors of the IGPF are 20% lower, and compared with GPF, the maximum error of the IGPF has declined 1.6% SOC. The SOC that is estimated by the IGPF is applied to estimate the SOP for battery, considering the restrictions from the peak SOC, the voltage, and the instruction manual. The result shows that the method based on the IGPF can successfully estimate SOP.

Keywords: lithium-ion battery; state estimation; state of charge; genetic particle filter; state of power (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/2/478/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/2/478/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:2:p:478-:d:310370

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().