Use of predictive information for Battery pack Thermal Management

A well-designed Battery Thermal Management System (BTMS) is essential for Li-ion batteries especially in high power applications such as electric vehicles. While most of the current BTMS technologies apply simple feedback control strategies, use of available predictive or preview information about traffic or weather patterns in BTMS is a rarely explored area. This paper focuses on the use of predictive information for a prototype BTMS that utilizes Thermoelectric Coolers (TEC) and a microchannel heat sink. A control-oriented nonlinear model is first developed for the system and a Nonlinear Model Predictive Control (NMPC) scheme is formulated to make it possible to use the knowledge of the predicted future drive cycle and the battery thermal system model for an efficient battery thermal management. Considering the availability of a predicted heat generation profile for the US06 drive cycle, it is shown that a considerable energy saving is possible with just a 30 seconds of prediction horizon without sacrificing the temperature tracking/regulation performance of the BTMS.