Experimental Validation of Electrothermal and Aging Parameter Identification for Lithium-Ion Batteries

Francesco Conte (), Marco Giallongo, Daniele Kaza, Gianluca Natrella, Ryohei Tachibana, Shinji Tsuji, Federico Silvestro and Giovanni Vichi
Additional contact information
Francesco Conte: Department of Engineering, Campus Bio-Medico University of Rome, Via Alvaro del Portillo 21, 00128 Roma, Italy
Marco Giallongo: Yanmar R&D Europe SRL, Viale Galileo 3/A, 50125 Firenze, Italy
Daniele Kaza: Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni, Università degli Studi di Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Gianluca Natrella: Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni, Università degli Studi di Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Ryohei Tachibana: Yanmar Holdings Co., Ltd., 2481 Umegahara, Maibara 5218511, Shiga, Japan
Shinji Tsuji: Yanmar Holdings Co., Ltd., 2481 Umegahara, Maibara 5218511, Shiga, Japan
Federico Silvestro: Dipartimento di Ingegneria Navale, Elettrica, Elettronica e delle Telecomunicazioni, Università degli Studi di Genova, Via all’Opera Pia 11a, 16145 Genova, Italy
Giovanni Vichi: Yanmar R&D Europe SRL, Viale Galileo 3/A, 50125 Firenze, Italy

Energies, 2024, vol. 17, issue 10, 1-30

Abstract: Modeling and predicting the long-term performance of Li-ion batteries is crucial for the effective design and efficient operation of integrated energy systems. In this paper, we introduce a comprehensive semi-empirical model for Li-ion cells, capturing electrothermal and aging features. This model replicates the evolution of cell voltage, capacity, and internal resistance, in relation to the cell actual operating conditions, and estimates the ongoing degradation in capacity and internal resistance due to the battery use. Thus, the model articulates into two sub-models, an electrothermal one, describing the battery voltage, and an aging one, computing the ongoing degradation. We first propose an approach to identify the parameters of both sub-models. Then, we validate the identification procedure and the accuracy of the electrothermal and aging models through an experimental campaign, also comprising two real cycle load tests at different temperatures, in which real measurements collected from real Li-ion cells are used. The overall model demonstrates good performances in simulating battery characteristics and forecasting degradation. The results show a Mean Absolute Percentage Error (MAPE) lower than 1% for battery voltage and capacity, and a maximum absolute error on internal resistance that is on par with the most up-to-date empirical models. The proposed approach is therefore well-suited for implementation in system modeling, and can be employed as an informative tool for enhancing battery design and operational strategies.

Keywords: Li-ion battery degradation; semi-empirical model; parameter identification; performance and lifetime prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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