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

authored by: 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.
Organisation(s): Faculty of Electrical Engineering and Computer Science
External Organisation(s): Nanyang Technological University (NTU)
Type: Article
Journal: IEEE Transactions on Industrial Electronics
Volume: 71
Pages: 5844-5852
No. of pages: 9
ISSN: 0278-0046
Publication date: 06.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Control and Systems Engineering, Electrical and Electronic Engineering
Sustainable Development Goals: SDG 7 - Affordable and Clean Energy
Electronic version(s): https://doi.org/10.1109/TIE.2023.3292868 (Access: Closed)

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",
}