State of Charge and Temperature Joint Estimation Based on Ultrasonic Reflection Waves for Lithium-Ion Battery Applications

Abstract

Accurate estimation of the state of charge (SOC) and temperature of batteries is essential to ensure the safety of energy storage systems. However, it is very difficult to obtain multiple states of the battery with fewer sensors. In this paper, a joint estimation method for a lithium iron phosphate battery’s SOC and temperature based on ultrasonic reflection waves is proposed. A piezoelectric transducer is affixed to the surface of the battery for ultrasonic–electric transduction. Ultrasonic signals are excited at the transducer, transmitted through the battery, and transmitted back to the transducer by reaching the underside of the battery. Feature indicator extraction intervals of the battery state are determined by sliding–window matching correlation analysis. Virtual samples are used to expand the data after feature extraction. Finally, a backpropagation (BP) neural network model is applied to the multistate joint estimation of a battery in a wide temperature range. According to the experimental results, the root mean square error (RMSE) of the lithium-ion battery’s SOC and temperature estimation results is 7.42% and 0.40 °C, respectively. The method is nondestructive and easy to apply in battery management systems. Combined with the detection of gas production inside the battery, this method can improve the safety of the battery system.