Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents various challenges. Generally, this task is handled by a battery management system (BMS). Therefore, this paper provides a brief introduction to the key battery state parameters, such as the state of charge (SOC), state of health (SOH), and state of power (SOP). Subsequently, after a brief overview of BMS structural and software architectures, this work focuses on a detailed description of equivalent circuit models (ECMs) and artificial neural networks (ANNs), which represent part of the modeling approaches available in the literature, providing a characterization of the complex and nonlinear dynamics underlying lithium-ion batteries. These approaches are systematically evaluated, including hybrid methods to highlight their respective advantages, limitations, and suitability for different BMS functionalities.
Laudani, D.P., Milillo, D., Sabino, L., Quercio, M., Riganti Fulginei, F. (2026). A Comprehensive Review of Equivalent Circuit Models and Neural Network Models for Battery Management Systems. BATTERIES, 12(1) [10.3390/batteries12010037].
A Comprehensive Review of Equivalent Circuit Models and Neural Network Models for Battery Management Systems
Laudani D. P.Methodology
;Milillo D.Validation
;Sabino L.Visualization
;Quercio M.Writing – Review & Editing
;Riganti Fulginei F.
Supervision
2026-01-01
Abstract
Lithium-ion batteries are the most widely used electrochemical energy storage technology due to their excellent performance. They play a crucial role in enabling the widespread adoption of sustainable transportation and renewable energy storage. Comprehensive battery monitoring, encompassing both performance and safety aspects, presents various challenges. Generally, this task is handled by a battery management system (BMS). Therefore, this paper provides a brief introduction to the key battery state parameters, such as the state of charge (SOC), state of health (SOH), and state of power (SOP). Subsequently, after a brief overview of BMS structural and software architectures, this work focuses on a detailed description of equivalent circuit models (ECMs) and artificial neural networks (ANNs), which represent part of the modeling approaches available in the literature, providing a characterization of the complex and nonlinear dynamics underlying lithium-ion batteries. These approaches are systematically evaluated, including hybrid methods to highlight their respective advantages, limitations, and suitability for different BMS functionalities.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
