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2020•Unpublished venueRequires access

A Comparison between State of Charge Estimation Methods: Extended Kalman Filter and Unscented Kalman Filter

Adelina Ioana Ilieş, Gabriel Chindriș, Dan Pitică

Open publisher page 21 citations

Abstract

The Battery Management System (BMS) plays an essential role in the optimal and safe operation of a battery. One task performed by the BMS is the battery parameters monitoring. State of charge is a critical parameter that indicates the amount of charge contained in a battery. An accurate estimation of the state of charge of the battery is important not only for informing the user but also in establishing a control strategy for keeping the battery parameters within the safe limits in order to maximize its lifespan. In this paper, a comparison in terms of performance between two variations of the Kalman filter (the Extended Kalman filter and the Unscented Kalman filter) for state of charge estimation is presented.

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What this paper is about

The Battery Management System (BMS) plays an essential role in the optimal and safe operation of a battery. One task performed by the BMS is the battery parameters monitoring. State of charge is a critical parameter that indicates the amount of charge contained in a battery. An accurate estimation of the state of charge of the battery is important not only for informing the user but also in establishing a control strategy for keeping the battery parameters within the safe limits in order to maximize its lifespan. In this paper, a comparison in terms of performance between two variations of the Kalman filter (the Extended Kalman filter and the Unscented Kalman filter) for state of charge estimation is presented.

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OpenAlex reports 21 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The Battery Management System (BMS) plays an essential role in the optimal and safe operation of a battery. One task performed by the BMS is the battery parameters monitoring. State of charge is a critical parameter that indicates the amount of charge contained in a battery. An accurate estimation of the state of charge of the battery is important not only for informing the user but also in establishing a control strategy for keeping the battery parameters within the safe limits in order to maximize its lifespan. In this paper, a comparison in terms of performance between two variations of the Kalman filter (the Extended Kalman filter and the Unscented Kalman filter) for state of charge estimation is presented.

Key concepts: Kalman filter, State of charge, Fast Kalman filter, Extended Kalman filter, Battery (electricity), Control theory (sociology), Alpha beta filter, Invariant extended Kalman filter

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