2022
DOI: 10.3390/su14127412
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A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries

Abstract: With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different a… Show more

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Cited by 13 publications
(7 citation statements)
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“…Thus, it can estimate the temperature gradient in a PCM using only the input and output temperature and flow rate measurements of the HTF circulated through the storage unit. The use of observers has been analysed mainly for electrical energy storage systems [37][38][39], with limited application in thermal stores. Ref.…”
Section: Introductionmentioning
confidence: 99%
“…Thus, it can estimate the temperature gradient in a PCM using only the input and output temperature and flow rate measurements of the HTF circulated through the storage unit. The use of observers has been analysed mainly for electrical energy storage systems [37][38][39], with limited application in thermal stores. Ref.…”
Section: Introductionmentioning
confidence: 99%
“…PFC is crucial for ensuring that the grid current is in phase with the voltage, which helps reduce power loss and increase AC power production. Moreover, a comprehensive comparison of sigma-point Kalman filters and smooth variable structure filters, with applications in robotics and aerospace systems [94][95][96][97][98][99][100][101][102][103][104][105][106][107][108][109][110][111][112]. Recent studies have investigated a variety of adaptive Kalman filters in addition to other estimation methods in order to improve the accuracy of state estimation for a wide variety of applications.…”
Section: Introductionmentioning
confidence: 99%
“…It uses a recursive algorithm to continually update the estimate of the system's state as new measurements become available, making it well-suited for real-time applications. Additionally, the KF can handle nonlinear systems through extensions such as the Extended Kalman Filter (EKF) [38][39][40][41][42][43] and the Unscented Kalman Filter (UKF) [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61], which provide approximate solutions for nonlinear systems. However, it is important to note that the KF has limitations.…”
Section: Introductionmentioning
confidence: 99%