Lithium-ion (Li-ion) batteries may fail through thermal runaway caused by increased temperature. It is thus important to monitor battery temperature for prevention of the battery failure. Currently, thermal monitoring of the battery for electric vehicles (EVs) is being conducted by multiple thermostats. As the size of battery system increases and the cells are closely packed to exploit high power density, the number of thermostats is also increased to monitor the battery system. However, this increased number of sensors enhances the probability of the sensor malfunction, which prevents robust thermal monitoring, and causes increased maintenance cost and customer complaints. This paper thus proposes an online applicable temperature prediction model for EV battery pack while minimizing the number of sensors and keeping the monitoring capability. This was possible with three ideas: (a) devising battery thermal characterization test under various operating conditions, (b) development of the online-applicable temperature prediction model using artificial neural network (ANN), and (c) validation of the temperature prediction model. The proposed temperature prediction model was demonstrated with the EV battery pack that consists of twelve battery modules.
Evaluation of hysteresis expressions in a lumped voltage prediction model of a NiMH battery system in stationary storage applications
