Online State Estimation of Lithium Ion B

The fossil fuel downsides and the energy crisis are the driving force toward clean and sustainable energy sources. Energy storage technology is a crucial solution to the shortcomings of renewable energy sources such as availability and portability. This study aims to use the continuum electrochemical lithium-ion battery model in addition to Kalman filter algorithms to predict battery external and internal dynamic behavior. The diffusion and migration of Li-ion in the electrolyte, as well as the charge balance inside each solid/liquid phase, are considered to simulate the battery's dynamic behavior. The proposed simplified model can predict the Li-ion battery's behavior accurately almost fifteen times faster than the full-order model. The efficiency and robustness of the nonlinear electrochemical model-based Kalman filter were tested under two different kinds of input current loads. Results reveal that the estimator can predict the battery's macro and micro scale states with and without error in the model's initial conditions. Notably, when the estimator starts simulation with a 30% error in the initial conditions, the estimated state of charge (SOC) can reach less than 1% error with real value in less than 50 seconds.