Health monitoring of Li-ion batteries: A particle filtering approach

The failures of a Li-ion battery can generally be classified into abrupt and gradual fault categories. The abrupt faults which can lead to hazardous situations are usually monitored and avoided. Gradual faults, that can result from battery´s aging and degradation, and are best manifested in the battery electrochemical parameters, are however more difficult to monitor and are thus less considered in health monitoring of Li-ion batteries. Nevertheless, their monitoring is not only important for the safety of battery operation but also are important in the process of battery´s state of life (SoL) prediction. This paper presents a model-based approach toward the health monitoring of Li-ion battery. It is shown that how the state and parameter estimation of electrochemical models of Li-ion batteries can be utilized for their fault diagnostics. For this purpose, particle filtering methods are developed for state/parameter estimation. Simulation results are also provided to study the effectiveness of the proposed method for a few of the common faults occurring in Li-ion batteries.