A real-time condition monitoring for lithium-ion batteries using a low-price microcontroller

Model-based condition monitoring for lithium-ion (Li-ion) batteries involves estimating physical model parameters and operational states such state of charge (SOC) and state of health (SOH), and is crucial for building high-performance and safety-critical battery systems. This paper proposes a real-time model-based condition monitoring algorithm based on a second-order RC electrical circuit battery model. The proposed method consists of an extended Kalman filter (EKF)-based online parameter identification algorithm and a smooth variable structure filter (SVSF)-based state estimation algorithm. The two filters are systematically integrated to cooperate each other and named hybrid filter (HF). The HF is not only compared with a dual extended Kalman filter (DEKF) in numerical simulation studies, but also implemented in a low-priced microcontroller with a real-time battery cell simulation testbed for a high-power Li-ion battery cell. The results show that the proposed HF can have less computational complexity and higher accuracy of SOC and capacity estimation than those of the conventional DEKF. Therefore, the proposed HF can be the alternative of DEKF as well as suitable for the real-time embedded battery management system (BMS) applications.