Virtual Sensor of Li-Ion Batteries in Electric Vehicles Using Data-Driven Analytic Thermal Solutions

verfasst von: Wei Guo Foo, Rufan Yang, Franz Erich Wolter, Hung Dinh Nguyen
Abstract: Lithium-ion batteries, especially for electric vehicles (EVs), present safety risks, suffer poor performances, and undergo rapid degradation when operating under high temperatures. This, therefore, necessitates thermal monitoring for timely intervention. However, computations for this purpose can be very expensive and difficult to implement in real time. To overcome this problem, we establish a framework based on closed-form solutions to heat equations to estimate important parameters based on measurement data. They will be used for deducing heat generation rates for constructing forward-monitoring models for estimation. Our results show that the root-mean-square error between the estimated and actual temperature is at most 0.23 for sensor input interval between 50 and 60 s over the monitoring time of 1200 s, both with and without varying input currents. In addition, our proposed method achieves computations circa 350 times faster than that of finite element methods.
Organisationseinheit(en): Fakultät für Elektrotechnik und Informatik
Externe Organisation(en): Nanyang Technological University (NTU)
Typ: Artikel
Journal: IEEE Transactions on Industrial Electronics
Band: 71
Seiten: 5844-5852
Anzahl der Seiten: 9
ISSN: 0278-0046
Publikationsdatum: 06.2024
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Steuerungs- und Systemtechnik, Elektrotechnik und Elektronik
Ziele für nachhaltige Entwicklung: SDG 7 – Erschwingliche und saubere Energie
Elektronische Version(en): https://doi.org/10.1109/TIE.2023.3292868 (Zugang: Geschlossen)

BibTeX

@article{4fed4b08a37042dfa8e5678804a6565f,
title = "Virtual Sensor of Li-Ion Batteries in Electric Vehicles Using Data-Driven Analytic Thermal Solutions",
abstract = "Lithium-ion batteries, especially for electric vehicles (EVs), present safety risks, suffer poor performances, and undergo rapid degradation when operating under high temperatures. This, therefore, necessitates thermal monitoring for timely intervention. However, computations for this purpose can be very expensive and difficult to implement in real time. To overcome this problem, we establish a framework based on closed-form solutions to heat equations to estimate important parameters based on measurement data. They will be used for deducing heat generation rates for constructing forward-monitoring models for estimation. Our results show that the root-mean-square error between the estimated and actual temperature is at most 0.23 for sensor input interval between 50 and 60 s over the monitoring time of 1200 s, both with and without varying input currents. In addition, our proposed method achieves computations circa 350 times faster than that of finite element methods.",
keywords = "Battery management systems, heat equation and closed-form solutions, NMC battery, thermal analysis, thermal management of batteries",
author = "Foo, {Wei Guo} and Rufan Yang and Wolter, {Franz Erich} and Nguyen, {Hung Dinh}",
note = "Funding Information: This work was supported in part by the National Research Foundation Singapore, in part by the Energy Market Authority through Energy Programme under EP Award EMA-EP004-EKJGC-0003, in part by the Ministry of Education Singapore under Award AcRF TIER 1 RG60/22, and in part by the Intra-CREATE Seed Fund under Award NRF2022-ITS010-0005. ",
year = "2024",
month = jun,
doi = "10.1109/TIE.2023.3292868",
language = "English",
volume = "71",
pages = "5844--5852",
journal = "IEEE Transactions on Industrial Electronics",
issn = "0278-0046",
publisher = "IEEE Industrial Electronics Society",
number = "6",
}