Local-Properties-Embedding-Based Nonlinear Spatiotemporal Modeling for Lithium-Ion Battery Thermal Process
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
Original language | English |
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Pages (from-to) | 9767-9776 |
Journal / Publication | IEEE Transactions on Industrial Electronics |
Volume | 65 |
Issue number | 12 |
Online published | 22 Mar 2018 |
Publication status | Published - Dec 2018 |
Link(s)
Abstract
The temperature distribution of a lithium-ion battery (LIB) belongs to a nonlinearly distributed parameter system (DPS), which is infinite dimensional in the space direction. Modeling of such systems often leads to the following challenges: time/space-coupled dynamics; time-varying dynamics; and spatial nonlinearity. For the first two challenges, Karhunen–Loeve (KL) decomposition ` based data-driven spatiotemporal models have been widely researched and successfully applied to industrial thermal processes. However, this method is a global linear model reduction method that ignores the nonlinear properties of measurements. This will prohibit the model performance of a strong nonlinear DPS, which has strong spatial nonlinearity refers to the aforementioned third challenge. To address this problem, a local-properties-embedding-based modeling method is developed for the nonlinear DPS. First, the finite-dimensional nonlinear spatial basis functions that can be the representative of the nonlinear feature of the original space are learned by the local-properties-embeddingbased method. Then, the low-dimensional representative can be derived using the time/space separation and the unknown temporal coefficients can be identified through the traditional neural learning algorithm. Finally, the spatiotemporal temperature distribution can be reconstructed by the time/space synthesis. Since the nonlinear structure feature of the spatiotemporal datasets has been considered, the proposed modeling method can be more effective than the traditional KL-based method for modeling the nonlinear DPS. Numerical simulations on the LIB showed that the proposed methods are more robust than the KL-based method in both the time direction and the space direction.
Research Area(s)
- Distributed parameter systems, Lithium-ion battery thermal process, Local properties embedding, Model reduction, Neural learning
Citation Format(s)
Local-Properties-Embedding-Based Nonlinear Spatiotemporal Modeling for Lithium-Ion Battery Thermal Process. / Xu, Kang-Kang; Li, Han-Xiong; Yang, Hai-Dong.
In: IEEE Transactions on Industrial Electronics, Vol. 65, No. 12, 12.2018, p. 9767-9776.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review