With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-Time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-Time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.
Meng, J., Ricco, M., Luo, G., Swierczynski, M., Stroe, D., Stroe, A., et al. (2017). An overview of online implementable soc estimation methods for lithium-ion batteries. Institute of Electrical and Electronics Engineers Inc. [10.1109/OPTIM.2017.7975030].
An overview of online implementable soc estimation methods for lithium-ion batteries
Ricco, Mattia;
2017
Abstract
With the popularity of Electrical Vehicles (EVs), Lithium-ion battery industry is also developing rapidly. To ensure the battery safety usage and reduce the average lifecycle cost, accurate State Of Charge (SOC) tracking algorithms for real-Time implementation are required in different applications. Many different SOC estimation methods have been proposed in the literature. However, only few of them consider the real-Time applicability. This paper reviews the recently proposed online SOC estimation methods and classifies them into five categories, that is, Coulomb Counting methods (CCMs), Open Circuit Voltage methods (OCVMs), Impedance Spectroscopy Based Methods (ISBMs), Model Based methods (MBMs) and ANN Based methods (ANNBMs). Then, their principal features are illustrated and the main pros and cons are given. After that, SOC estimation methods are compared in terms of their accuracy, robustness, and computation burden. Finally, some conclusions are drawn.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.