Advanced Algorithm for SOC, Internal Resistance, and SOH Co-Estimation of Lithium-Titanate-Oxide Batteries Using Neural Networks
Source abstract
Abstract
Lithium-titanate-oxide batteries can reduce the long charging time of electric vehicles by offering fast charging capabilities. However, high charging currents require an accurate estimation of battery internal state to prevent early aging of the battery and dangerous situations. An accurate algorithm based on neural networks for the co-estimation of state of charge, internal resistance, and capacity state of health is proposed in this work. The algorithm is trained with synthetic data generated by an electric vehicle simulation platform running seven different standard driving cycles at various settings. The algorithm is then validated using an additional standard driving cycle, achieving, for state of charge, internal resistance, and capacity state of health, a root mean square error lower than 2%, 80 μΩ, and 2.9%, and a mean absolute percentage error lower than 3.4%, 4.4%, and 3.3%, respectively. The results obtained and the comparison with literature works indicate that the co-estimation algorithm proposed is able to estimate the considered quantities with very good accuracy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Advanced Battery Technologies Research · Advancements in Battery Materials · Conducting polymers and applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Marelli Europe S.p.A., Via del Timavo 33, 40131 Bologna, Italy
Dipartimento di Ingegneria dell’Informazione, University of Pisa, Via Caruso 16, 56122 Pisa, Italy
Marelli Corporation, Kodama Plant, 540-7 Kyoei, Kodama-cho, Honjo-City 367-0206, Saitama, Japan
Funding
European Union Next-GenerationEU · National Recovery and Resilience Plan (NRRP) · Ministero dell’Università e della Ricerca (MUR)
References (34)
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