Details
| Original language | English |
|---|---|
| Pages (from-to) | 5844-5852 |
| Number of pages | 9 |
| Journal | IEEE Transactions on Industrial Electronics |
| Volume | 71 |
| Issue number | 6 |
| Early online date | 19 Jul 2023 |
| Publication status | Published - Jun 2024 |
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
ASJC Scopus subject areas
- Engineering(all)
- Control and Systems Engineering
- Engineering(all)
- Electrical and Electronic Engineering
Sustainable Development Goals
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In: IEEE Transactions on Industrial Electronics, Vol. 71, No. 6, 06.2024, p. 5844-5852.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Virtual Sensor of Li-Ion Batteries in Electric Vehicles Using Data-Driven Analytic Thermal Solutions
AU - Foo, Wei Guo
AU - Yang, Rufan
AU - Wolter, Franz Erich
AU - Nguyen, Hung Dinh
N1 - 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.
PY - 2024/6
Y1 - 2024/6
N2 - 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.
AB - 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.
KW - Battery management systems
KW - heat equation and closed-form solutions
KW - NMC battery
KW - thermal analysis
KW - thermal management of batteries
UR - http://www.scopus.com/inward/record.url?scp=85165292425&partnerID=8YFLogxK
U2 - 10.1109/TIE.2023.3292868
DO - 10.1109/TIE.2023.3292868
M3 - Article
AN - SCOPUS:85165292425
VL - 71
SP - 5844
EP - 5852
JO - IEEE Transactions on Industrial Electronics
JF - IEEE Transactions on Industrial Electronics
SN - 0278-0046
IS - 6
ER -