A Battery Management System Using Interleaved Pulse Charging With Charge and Temperature Balancing Based on NARX Network

Abstract

This paper proposes a battery management system, including a fast battery charger, battery aging diagnosis, and charge estimation and balancing. The charger adopts a single-inductor single-input dual-output architecture to achieve charge balancing among battery cells. Interleaved pulse charging is proposed to reduce the charging time and slow down the aging process of batteries as well. This method also significantly suppresses the variations of the temperature of battery cells and is beneficial to the implementation of charge balancing. An artificial neural network is proposed to detect the state of health (SOH) of battery cells and improve the accuracy of the state of charge (SOC) estimation. The prototype is implemented in TSMC 0.35- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> process and TensorFlow tools are used. Measurement results show that the interleaved pulse charging reduces 30% variation of the battery temperature and saves 24% charging time when charging four battery cells concurrently. A mean absolute error of SOC estimation of 0.35% is achieved in this work.