Lithium-Ion Battery Modelling and Online Battery Management Systems

摘要

Lithium-ion batteries represent one of the most crucial components to provide the power source for commercial products. To ensure a highly reliable of Li-ion battery, the battery State of Charge (SOC) is an essential function for battery management system (BMS). For these batteries, Equivalent circuits models represent the electrochemical parameters since they are capable of electrical elements represents an attractive approach and they are capable of modeling of lithium-ion (Li-ion) dynamically changing variables with reasonable computational power requirements. Consequently, electrochemical models provide a connection to the physical reactions that occur in the battery thus favored in the SOC and state of health (SOH) estimation in comparison to other modeling techniques. Many challenges are still facing the mass production of Li-ion battery; challenges include battery cost, safety, and reliability. These challenges can occur significantly moderated by incorporating an adequate online battery BMS. The online BMS is responsible for estimating, in real-time, the SOC of battery and SOH. This paper proposed an online SOC estimation method for Li-ion battery by using a particle filter (PF). Also, a comparison made with extended Kalman filter (EKF) with open circuit voltage, the curve exhibit the PF is a promising alternative for online estimation of SOC.