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http://dx.doi.org/10.7471/ikeee.2022.26.1.10

Real-time EKF-based SOC estimation using an embedded board for Li-ion batteries  

Lee, Hyuna (Dept. of Electronic Engineering, koreaTech)
Hong, Seonri (Dept. of Energy ICT Convergence Research, Korea Institute of Energy Research)
Kang, Moses (Dept. of Energy ICT Convergence Research, Korea Institute of Energy Research)
Sin, Danbi (Dept. of Computer Engineering, koreaTech)
Beak, Jongbok (Dept. of Energy ICT Convergence Research, Korea Institute of Energy Research)
Publication Information
Journal of IKEEE / v.26, no.1, 2022 , pp. 10-18 More about this Journal
Abstract
Accurate SOC estimation is an important indicator of battery operation strategies, and many studies have been conducted. The simulation method which was mainly used in previous studies, is difficult to conduct real-time SOC estimation like real BMS environment. Therefore, this paper aims to implement a real-time battery SOC estimation embedded system and analyze problems that can arise during the verification process. In environment consisting of two Raspberry Pi boards, SOC estimation with the EKF uses data measured by the Simscape battery model. Considering that the operating characteristics of the battery vary depend on the temperature, the results were analyzed at various ambient temperatures. It was confirmed that accurate SOC estimation was performed even when offset fault and packet loss occurred due to communication or sensing problems. This paper proposes a guide for embedded system strategies that enable real-time SOC estimation with errors within 5%.
Keywords
Real-time simulation; Embedded system; SOC estimation; Raspberry Pi; Extended Kalman Filter; Lithium-ion batteries;
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Times Cited By KSCI : 3  (Citation Analysis)
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