Comprehensive Review of Lithium-Ion Battery State of Charge Estimation by Sliding Mode Observers

Vahid Behnamgol, Mohammad Asadi, Mohamed A. A. Mohamed, Sumeet S. Aphale and Mona Faraji Niri ()
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Vahid Behnamgol: Energy Research Centre, Islamic Azad University of Damavand, Damavand 1477893780, Iran
Mohammad Asadi: Department of Electrical Engineering, Iran University of Science and Technology, Tehran 168463114, Iran
Mohamed A. A. Mohamed: Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK
Sumeet S. Aphale: Artificial Intelligence, Robotics and Mechatronic Systems (ARMS) Group, School of Engineering, University of Aberdeen, Aberdeen AB24 3FX, UK
Mona Faraji Niri: Energy Innovation Centre, WMG, University of Warwick, Coventry CV4 7AL, UK

Energies, 2024, vol. 17, issue 22, 1-39

Abstract: The state of charge (SoC) is a critical parameter in lithium-ion batteries and their alternatives. It determines the battery’s remaining energy capacity and influences its performance longevity. Accurate SoC estimation is essential for making informed charging and discharging decisions, mitigating the risks of overcharging or deep discharge, and ensuring safety. Battery management systems rely on SoC estimation, utilising both hardware and software components to maintain safe and efficient battery operation. Existing SoC estimation methods are broadly classified into direct and indirect approaches. Direct methods (e.g., Coulumb counting) rely on current measurements. In contrast, indirect methods (often based on a filter or observer) utilise a model of a battery to incorporate voltage measurements besides the current. While the latter is more accurate, it faces challenges related to sensor drift, computational complexity, and model inaccuracies. The need for more precise and robust SoC estimation without increasing complexity is critical, particularly for real-time applications. Recently, sliding mode observers (SMOs) have gained prominence in this field for their robustness against model uncertainties and external disturbances, offering fast convergence and superior accuracy. Due to increased interest, this review focuses on various SMO approaches for SoC estimation, including first-order, adaptive, high-order, terminal, fractional-order, and advanced SMOs, along with hybrid methods integrating intelligent techniques. By evaluating these methodologies, their strengths, weaknesses, and modelling frameworks in the literature, this paper highlights the ongoing challenges and future directions in SoC estimation research. Unlike common review papers, this work also compares the performance of various existing methods via a comprehensive simulation study in MATLAB 2024b to quantify the difference and guide the users in selecting a suitable version for the applications.

Keywords: lithium-ion batteries; state of charge estimation; battery model; sliding mode observer; uncertainty; chattering; stability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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