An Adaptive Neuro-Fuzzy Inference System (ANFIS) Based Model for the Temperature Prediction of Lithium-Ion Power Batteries
Abstract Li-ion batteries have been widely applied in the areas of personal electronic devices, stationary energy storage system and electric vehicles due to their high energy/power density, low self-discharge rate and long cycle life etc. For the better designs of both the battery cells and their thermal management systems, various numerical approaches have been proposed to investigate the thermal performance of power batteries. Without the requirement of detailed physical and thermal parameters of batteries, this article proposed a data-driven model using the adaptive neuro-fuzzy inference system (ANFIS) to predict the battery temperature with the inputs of ambient temperature, current and state of charge. Thermal response of a Li-ion battery module was experimentally evaluated under various conditions (i.e. ambient temperature of 0, 5, 10, 15 and 20 °C, and current rate of C/2, 1C and 2C) to acquire the necessary data sets for model development and validation.