 A novel state of charge estimation method for lithium-ion batteries based on bias compensationTiancheng Ouyang, Peihang Xu, Jingxian Chen, Zixiang Su, Guicong Huang and Nan Chen Energy, 2021, vol. 226, issue C Abstract: Accurate and efficient state-of-charge estimation for lithium-ion batteries are extremely crucial for electrical vehicles. A lot of researches make great progress on joint estimation algorithms. However, the combination of different algorithms brings too many design parameters, which reduces the accuracy of estimation and computational efficiency. In this paper, an adaptive H-infinity filter with bias compensation is proposed. Static condition and dynamic condition are set to verify the proposed algorithm in the experiments. In the dynamic condition, the proposed algorithm is verified and compared with the other three joint estimation algorithms at temperatures of 40 °C, 25 °C, 10 °C and 0 °C. Experiments show that the proposed algorithm achieves the highest estimation accuracy and calculation efficiency under two operating conditions and four temperatures, and the average time consumption of the proposed algorithm is reduced by 0.9%, 2.25% and 34.14%, respectively, compared with the other combinations of different algorithms. Keywords: State-of-charge; H-infinity filter; Bias compensation; Joint estimation; Lithium-ion battery (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:226:y:2021:i:c:s0360544221005971 DOI: 10.1016/j.energy.2021.120348 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.