Temperature field prediction of lithium-ion batteries using improved local tangent space alignment

Abstract

The temperature field prediction of lithium-ion batteries (LIBs) plays a crucial role in the safety of electric vehicles and their lifetime. However, it is essentially a nonlinear distributed parameter system (DPS), and suitable partial differential equations are difficult to obtain. Further, the existing methods are weak in handling nonlinear sparse data. To address this problem, a nonlinear DPS modeling approach based on kernel and weighted local tangent space alignment (KWLTSA) is proposed in this paper. Firstly, the nonlinear dimensionality reduction method of KWLTSA is used to construct basic functions (BFs) by using spatiotemporal data. Secondly, the low-order temporal coefficients are obtained according to the spatial BFs. Subsequently, the coefficients are fed into a backpropagation neural network (BP-NN) with the input signals for training. Finally, the temperature distribution of LIBs is obtained by spatiotemporal reconstruction. Comparative experiments are designed to estimate the model performance using existing evaluation metrics. The root mean square error (RMSE) is 0.1000 and the R-square (R2) is 0.9972 of the temperature prediction by the proposed scheme. The experimental results indicate that the proposed method is more robust than the traditional dimensionality reduction method.