Low Temperature, Current Dependent Battery State Estimation Using Interacting Multiple Model Strategy

Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Ka...

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Bibliographic Details
Main Authors: Marvin Messing, Sara Rahimifard, Tina Shoa, Saeid Habibi
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9478786/
Description
Summary:Lithium-ion battery State of Charge (SoC) estimation for Electric Vehicle (EV) applications must be robust and as accurate as possible to maximize battery utilization and ensure safe operation over a wide range of operating conditions. SoC estimation commonly utilizes filters such as the Extended Kalman Filter (EKF) which rely on battery models, usually in the form of Equivalent Circuit Models (ECM). At low temperatures the battery response to current draw becomes increasingly non-linear, resulting in amplified SoC estimation errors. In this study, current dependent SoC estimation at low temperature is proposed using an Interacting Multiple Model (IMM) filter with three ECMs covering a range of C-rates. The IMM is combined with the Smooth Variable Structure Filter (SVSF) to obtain robust SoC estimates within a SoC estimation error of 2%.
ISSN:2169-3536