 Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filterJinqing Linghu, Longyun Kang, Ming Liu, Xuan Luo, Yuanbin Feng and Chusheng Lu Energy, 2019, vol. 189, issue C Abstract: Accurate estimation for state-of-charge of the battery is very important for energy storage systems in electric vehicles and smart grids. To improve the accuracy and reliability of state-of-charge estimation, accurate model equations and a set of robust algorithm are necessary. Different from the commonly used method, this paper adopts a polynomial based on Gaussian function to build up the open circuit voltage function, and proposes an adaptive fifth-degree cubature Kalman filter algorithm to estimate the battery state-of-charge. Two typical driving cycles, including the dynamic stress test and the Worldwide harmonized Light Vehicles Test Cycle are applied to evaluate the performance of the proposed estimator. The results indicate that compared with the unscented Kalman filter and the adaptive cubature Kalman filter, the adaptive fifth-degree cubature Kalman filter can achieve higher state-of-charge estimation accuracy and better overcome the impact of large measurement error and initial error. Keywords: State-of-charge; Lithium-ion battery; Adaptive fifth-degree cubature Kalman filter; Gaussian function trinomial (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:189:y:2019:i:c:s0360544219318997 DOI: 10.1016/j.energy.2019.116204 Access Statistics for this article Energy is currently edited by Henrik Lund and Mark J. Kaiser More articles in Energy from Elsevier |
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