Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for ensuring the safety, reliability, and longevity of electric vehicles, battery energy storage systems, and other energy applications. This paper presents a comprehensive review of capacity-based SOH estimation algorithms, focusing...
Lithium-ion batteries have become the core energy carrier of electric vehicles and energy storage systems due to their high energy density and long cycle life. Their state of charge (SOC) is an important parameter in battery management systems, playing a key role in energy management, safety protection, and life pred...
The accurate estimation of State of Charge (SoC) is critical for safe, reliable, and optimistic operation of Battery Electric Vehicles (BEVs). Nevertheless, achieving robust SoC estimation is a big challenge because of nonlinear battery dynamics, parameter variability, sensor noise, and uncertain initial conditions. St...
Improving the performance and lifespan of lithium-ion batteries is a key challenge for the development of electric vehicles. However, accurately estimating the state of charge (SoC), state of health (SoH), and life cycle remains complex due to the electrical, thermal, and aging phenomena associated with these energy st...
This paper proposes a Koopman-based state-of-charge (SOC) observer for lithiumion batteries designed in a lifted linear space identified via extended dynamic mode decomposition with control (EDMDc). Accurate SOC estimation still remains challenging, since battery electrochemical dynamics are highly nonlinear and the...
This study aims to address the accuracy and robustness limitations of conventional single-model approaches for lithium-ion battery state-of-health (SOH) estimation. This paper proposes an enhanced improved Grey Wolf optimization (IGWO)-Transformer-GRU-based estimation framework. Health factors (HFs) of lithi...
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