Temperature‐Field Sparse‐Reconstruction of Lithium‐Ion Battery Pack Based on Artificial Neural Network and Virtual Thermal Sensor Technology
Fang Zheng, Mengyi Wang, Weifeng Hu, Kejing Chang, Bijiao Zhang
Abstract
To monitor the temperature of lithium‐ion battery packs more accurately with as few sensors as possible, a temperature‐field sparse‐reconstruction technique based on an artificial neural network (ANN) and a virtual thermal sensor (VTS) is proposed herein. 64 uniformly distributed temperature points of lithium‐ion battery packs in seven discharge cycles are measured by a thermometer, and the 64 sensors are further divided into real thermal sensors (RTS) and VTSs according to a certain number and spatial position relationship. In addition, the sensor compression rate (SCR) is defined, herein, to quantitatively measure the impact of the RTS number on temperature‐field sparse‐reconstruction. ANN is built and compared with linear regression (LR). The results show that the temperature‐field sparse‐reconstruction based on ANN and VTS can provide accurate and robust prediction; the maximum mean absolute error (MAE) of ANN is less than 0.1873 °C (SCR = 1.56%) in the experiment. ANN obtains better accuracy with fewer RTS compared with LR. In addition, the proposed principle of sensor layout design is effective. Herein, the temperature‐field sparse‐reconstruction of battery pack is realized without any knowledge of battery thermal properties, heat generation, or thermal boundary conditions, and the optimal number of RTS under given accuracy requirements are obtained.