This document presents a model-based virtual thermal sensor system for lithium-ion batteries in electric vehicles. The model uses a small number of physical sensors to estimate the real-time temperature distribution throughout the battery. The thermal model of the battery is developed and tuned using experimental data. Then, the tuned model combined with a Kalman filter estimates the temperature distribution under unknown initial conditions and uncertainty. The proposed virtual sensor system was experimentally validated on a 70-Ah lithium iron phosphate battery for automotive applications.

1. MODEL-BASED VIRTUAL THERMAL SENSORS FOR
LITHIUM-ION BATTERY IN EV APPLICATIONS
ABSTRACT
Continuous monitoring of temperature distribution in lithium-ion (Li-ion) batteries is
critical in preventing rapid degradation, mismatch in cell capacity, and potentially thermal
runaway. A model based on virtual thermal sensor (VTS) for automotive grade Li-ion batteries is
presented in this paper. This model, using a small number of physical sensors, is able to estimate
temperature distribution throughout the battery in real time. First, the thermal model of the
battery is developed and the characteristic parameters of the battery are tuned using the
prediction error minimization method. Then, the tuned model is combined with a Kalman filter
to estimate the temperature distribution of the battery under unknown initial values and model
uncertainty. The proposed model-based VTS has been experimentally validated on an
automotive grade 70-Ah lithium iron phosphate (LiFePO4) battery.