Locality Preserving Projections-Based Spatiotemporal Modeling of the Temperature Distribution of Lithium-Ion Batteries

Abstract

The temperature distribution is one of the key factors affecting the safety, capacity fade, and optimal use of lithium-ion batteries (LIBs). However, it is a challenging task to develop an accurate approximation model for estimating this distribution, owing to the inherent nature of thermal processes, including time/space coupling, infinite dimensions, and strong nonlinearity. Therefore, this paper proposes a novel locality preserving projections (LPP)-based spatiotemporal modeling method to address this problem. First, an orthogonal enhanced LPP (OELPP) method is proposed for time/space decoupling. With this novel reduction dimension technique, the spatiotemporal temperature variables are separated into spatial basis functions (SBFs) and low-dimensional time variables. Then, to handle the temporal nonlinearity, a broad learning system with kernel-based manifold features (BLS-KMF) is designed to construct a dynamic temporal model. To improve the modeling performance, the kernel trick and the intrinsic manifold information are introduced to the proposed BLS-KMF to learn the mapped features. Finally, with the time/space synthesis, the battery temperature distribution can be reconstructed. Experimental results on a 32 Ah ternary lithium-ion battery illustrate the superior performance and effectiveness of the proposed method.