Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling.

Lucaferri, V., Quercio, M., Laudani, A., Riganti Fulginei, F. (2023). A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems. ENERGIES, 16(23) [10.3390/en16237807].

A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems

Quercio M.;Riganti Fulginei F.
2023-01-01

Abstract

Battery state estimation is fundamental to battery management systems (BMSs). An accurate model is needed to describe the dynamic behavior of the battery to evaluate the fundamental quantities, such as the state of charge (SOC) or the state of health (SOH). This paper presents an overview of the most commonly used battery models, the equivalent electrical circuits, and data-driven ones, discussing the importance of battery modeling and the various approaches used to model lithium batteries. In particular, it provides a detailed analysis of the electrical circuit models commonly used for lithium batteries, including equivalent circuit and thermal models. Furthermore, a comprehensive overview of data-driven approaches is presented. The advantages and limitations of each type of model are discussed. Finally, the paper concludes with a discussion of current research trends and future directions in the field of battery modeling.
2023
Lucaferri, V., Quercio, M., Laudani, A., Riganti Fulginei, F. (2023). A Review on Battery Model-Based and Data-Driven Methods for Battery Management Systems. ENERGIES, 16(23) [10.3390/en16237807].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11590/465089
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