The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is currently assessed with different methods in a vast variety of applications. This paper presents the design and assessment of two SOC estimation methods, based on Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) algorithms for Lithium-ion batteries used in vehicular applications. The paper validates the two proposed approaches with experimental data collected during a laboratory test campaign. The obtained results are compared in terms of estimation accuracy, proving the feasibility of the considered algorithms. Moreover, the paper describes the retained software architectures and the design procedure related to the two proposed techniques based on Artificial Intelligence (AI). In detail, the retained Lithium-ion battery is a 21.6V 3.3Ah battery pack that is used as an energy module for vehicular applications. The considered battery module is numerically modeled with a 2𝑛𝑑 order RC equivalent Thevenin model to collect a sufficient amount of data for the algorithms design phase. The model parameters are identified with a grey-box approach based on a non-linear least squares algorithm designed to accurately estimate the battery SOC with both the ANN-based and SVM-based methods. Specifically, the resulting mean prediction error is always below 2.5% and 3.5% for the ANN-based and SVM-based algorithms, respectively.

Assessment of State of Charge Estimation Methods Based on Neural Networks and Support Vector Machine for Lithium-Ion Batteries Used in Vehicular Applications / Luciani, Sara; Feraco, Stefano; Silvagni, Mario; Bonfitto, Angelo; Amati, Nicola; Tonoli, Andrea. - ELETTRONICO. - (2022). ((Intervento presentato al convegno ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference tenutosi a St. Louis, Missouri, USA nel 14-17 August, 2022 [10.1115/DETC2022-89454].

Assessment of State of Charge Estimation Methods Based on Neural Networks and Support Vector Machine for Lithium-Ion Batteries Used in Vehicular Applications

Luciani, Sara;Feraco, Stefano;Silvagni, Mario;Bonfitto, Angelo;Amati, Nicola;Tonoli, Andrea
2022

Abstract

The State of Charge (SOC) estimation in Lithium-ion batteries is a challenging task that is currently assessed with different methods in a vast variety of applications. This paper presents the design and assessment of two SOC estimation methods, based on Artificial Neural Networks (ANNs) and Support Vector Machine (SVM) algorithms for Lithium-ion batteries used in vehicular applications. The paper validates the two proposed approaches with experimental data collected during a laboratory test campaign. The obtained results are compared in terms of estimation accuracy, proving the feasibility of the considered algorithms. Moreover, the paper describes the retained software architectures and the design procedure related to the two proposed techniques based on Artificial Intelligence (AI). In detail, the retained Lithium-ion battery is a 21.6V 3.3Ah battery pack that is used as an energy module for vehicular applications. The considered battery module is numerically modeled with a 2𝑛𝑑 order RC equivalent Thevenin model to collect a sufficient amount of data for the algorithms design phase. The model parameters are identified with a grey-box approach based on a non-linear least squares algorithm designed to accurately estimate the battery SOC with both the ANN-based and SVM-based methods. Specifically, the resulting mean prediction error is always below 2.5% and 3.5% for the ANN-based and SVM-based algorithms, respectively.
978-0-7918-8620-5
File in questo prodotto:
File Dimensione Formato  
v001t01a014-detc2022-89454.pdf

non disponibili

Tipologia: 2a Post-print versione editoriale / Version of Record
Licenza: Non Pubblico - Accesso privato/ristretto
Dimensione 1.77 MB
Formato Adobe PDF
1.77 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2973081
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo