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 -