View in EDS

Multi-point temperature estimation of prismatic lithium-ion batteries based on ASRUKF across wide operating conditions.

Saved in:
Bibliographic Details
Title: Multi-point temperature estimation of prismatic lithium-ion batteries based on ASRUKF across wide operating conditions.
Authors: Shen, Jiangwei1,2 (AUTHOR) shenjiangwei6@163.com, Li, Xijin1 (AUTHOR) lixijin1@stu.kust.edu.cn, Shu, Xing2 (AUTHOR) shuxing@cqut.edu.cn, Liu, Yonggang3 (AUTHOR) yliuyg@cqu.edu.cn, Xia, Xuelei1 (AUTHOR) xxl92@stu.kust.edu.cn, Wei, Fuxing1 (AUTHOR) wfx@kust.edu.cn, Chen, Zheng1 (AUTHOR) chen@kust.edu.cn
Source: International Journal of Heat & Mass Transfer. Sep2026, Vol. 265, pN.PAG-N.PAG. 1p.
Subjects: Temperature measurements, Kalman filtering, Lithium-ion batteries, Thermal stability, Battery management systems, Temperature sensors
Abstract: Prismatic lithium-ion batteries (LIBs) feature large capacities and uneven heat generation. These characteristics can induce local thermal runaway, which may subsequently propagate across battery surfaces. To ensure accurate and real-time temperature monitoring across various operating regions, this study proposes a multi-point temperature estimation method. This method targets both surface and internal nodes using the adaptive square root unscented Kalman filter (ASRUKF). First, an electro-thermal coupled model is established. This framework integrates a second-order resistance–capacitance (2RC) equivalent circuit model, a three-source heat generation model, and a two-state lumped thermal model. The ASRUKF algorithm is then applied to estimate the state of temperature (SOT) at multiple spatial locations. Subsequently, the estimated SOT serves as a feedback variable to update the state of charge (SOC). The updated SOC is directly utilized to calculate the current open-circuit voltage. This sequential process facilitates continuous model parameter identification, thereby enabling real-time parameter updates and online adjustments. Experimental validation confirms that the proposed method provides reliable multi-point SOT estimation. The maximum absolute error (MAXE) remains strictly below 0.5 ℃. This result demonstrates a distinct improvement in estimation accuracy compared to existing approaches. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are maintained within 0.21 ℃. These metrics were evaluated under dynamic conditions across a wide operating temperature range (−10℃ to 50 ℃). Overall, the findings indicate the high accuracy, broad environmental adaptability, and robust performance of the proposed algorithm. • A multi-point online temperature estimation method is proposed and validated. • An electro-thermal coupling model with temperature adaptability is established. • The adaptive square root unscented Kalman filter enhances estimation accuracy. • The mean absolute error of the state of temperature estimation is within 0.21°C. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Heat & Mass Transfer is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Prismatic lithium-ion batteries (LIBs) feature large capacities and uneven heat generation. These characteristics can induce local thermal runaway, which may subsequently propagate across battery surfaces. To ensure accurate and real-time temperature monitoring across various operating regions, this study proposes a multi-point temperature estimation method. This method targets both surface and internal nodes using the adaptive square root unscented Kalman filter (ASRUKF). First, an electro-thermal coupled model is established. This framework integrates a second-order resistance–capacitance (2RC) equivalent circuit model, a three-source heat generation model, and a two-state lumped thermal model. The ASRUKF algorithm is then applied to estimate the state of temperature (SOT) at multiple spatial locations. Subsequently, the estimated SOT serves as a feedback variable to update the state of charge (SOC). The updated SOC is directly utilized to calculate the current open-circuit voltage. This sequential process facilitates continuous model parameter identification, thereby enabling real-time parameter updates and online adjustments. Experimental validation confirms that the proposed method provides reliable multi-point SOT estimation. The maximum absolute error (MAXE) remains strictly below 0.5 ℃. This result demonstrates a distinct improvement in estimation accuracy compared to existing approaches. Furthermore, the mean absolute error (MAE) and root mean square error (RMSE) are maintained within 0.21 ℃. These metrics were evaluated under dynamic conditions across a wide operating temperature range (−10℃ to 50 ℃). Overall, the findings indicate the high accuracy, broad environmental adaptability, and robust performance of the proposed algorithm. • A multi-point online temperature estimation method is proposed and validated. • An electro-thermal coupling model with temperature adaptability is established. • The adaptive square root unscented Kalman filter enhances estimation accuracy. • The mean absolute error of the state of temperature estimation is within 0.21°C. [ABSTRACT FROM AUTHOR]
ISSN:00179310
DOI:10.1016/j.ijheatmasstransfer.2026.128804