Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications

Gaga, Ahmed; Benssassi, Hicham; Errahimi, Fatima; Es-Sbai, Najia

doi:10.15866/ireaco.v10i4.11393

Cited by 3 publications

(1 citation statement)

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“…The authors in [10] have proposed an automatic Extended Kalman Filter tuning for UAS path following in turbulent air. In [11], a Kalman observer has been employed in order to estimate the state of charge of a battery using an adaptive unscented Kalman filter for photovoltaics systems. Several approaches to steady state estimation and dynamic state estimation based on PMUs have been proposed during the last years with promising results [12].…”

Section: Introductionmentioning

confidence: 99%

Online Dynamic Assessment of System Stability in Power Systems Using the Unscented Kalman Filter

Moreno‐Chuquen

Flórez-Cediel

2019

IREE

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Dynamic state estimation on of power systems stability based on PhasorMeasurement Units (PMUs) data is a requirement in order to assess power system security. This paper addresses the state estimation problem from a strong mathematical point of view to be applied for estimation on real time. Specifically, this paper formulates a framework based on the Unscented Kalman Filter (UFK) in order to estimate in real-time if the angle stability is compromised or approaching instability. This paper proposes a predicting window as a time interval to forecast the rotor angle using real-time information. The framework uses the generator rotor angles and the electrical angular velocity as state variables, given that the power output of the generators is measured by PMUs and that the rotor movement equations are separated from the network ones. The estimation of the angle stability can provide meaningful information needed to evaluate and enhance the power system security. In order to note the performance for the UKF to assess and forecast online system stability for power systems, the UKF is tested when there are not measurements available. In this case, the UKF performs a forecasting on the angle in a onesecond window without available data. Emulated PMUs sampling data have been used for carrying the simulations and have been validated using the 9-IEEE buses test system. The results confirm the method and the performance of the estimation. The proposed approach provides an effective tool for real-time environment for security.

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Section: Introductionmentioning

confidence: 99%

Online Dynamic Assessment of System Stability in Power Systems Using the Unscented Kalman Filter

Moreno‐Chuquen

Flórez-Cediel

2019

IREE

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State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications

Sassi

Errahimi

Es-Sbai

2020

Journal of Energy Storage

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Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review

Gaga

Tannouche

Mehdaoui

et al. 2022

Energy Harvesting and Systems

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In recent decades, electric vehicles (EVs) have been garnering tremendous popularity because of their improved performance and efficiency, as well as new concerns about global warming, greenhouse gas emissions, and the depletion of fossil fuels. Extensive use of electric vehicles has already been seen in the automotive industry, especially because of the CO2 emissions and global environmental challenges they help with. A lot of attention has been paid to lithium-ion batteries for their numerous benefits, including lightweight, fast charging, high energy density, extended lifespan, and low self-discharge. This study covers the state of charge (SOC) estimation and management of the lithium-ion battery for sustainable future electric vehicle applications. The importance of adopting a lithium-ion battery management system (BMS) is shown, which guarantees a stable and safe operation and assesses the battery state of charge (SOC). According to the review, the SOC is an important parameter as it denotes the battery’s remaining charge and influences charging and discharging tactics. Additionally, it is shown that existing lithium-ion battery SOC has a positive effect on ensuring the safe and efficient operation of electric vehicles with their charging and discharging capacities. Despite these hurdles, batteries still have certain limitations, such as complex electro-chemical reactions, decreased performance, and inaccuracies in enhancing battery performance and life. This paper thoroughly reviews the approaches used to estimate or capture (SOC) parameters by focusing on the calculation model or algorithm, advantages, disadvantages, and estimation error. It describes a number of aspects and obstacles that have been identified and suggestions for their use in the development of BMS and for estimating SOC in future EV applications are offered. The rising attempts to improve the high-tech future EV applications, SOC calculation method, and energy management system will be enhanced by this review’s highlight insights.

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Battery State of Charge Estimation Using an Adaptive Unscented Kalman Filter for Photovoltaics Applications

Cited by 3 publications

References 0 publications

Online Dynamic Assessment of System Stability in Power Systems Using the Unscented Kalman Filter

Online Dynamic Assessment of System Stability in Power Systems Using the Unscented Kalman Filter

State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications

Methods for estimating lithium-ion battery state of charge for use in electric vehicles: a review

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