A Reduced-Order Electrochemical Model of Li-Ion Batteries for Control and Estimation Applications

In this paper, a reduced-order electrochemical model of lithium-ion batteries is developed for control and estimation applications through analytical model order reduction based on a Galerkin projection method. The governing diffusion partial differential equations in the liquid and solid phases are approximated into low-order systems of ordinary differential equations while the physical meaning of all model parameters is preserved, allowing one to perform state and parameter estimation. The selection of basis functions for the Galerkin projection method and model order truncation is carefully determined based on analysis both in the frequency and time domains. With the reduced-order diffusion models in the liquid and solid phases, an extended single particle model incorporating the electrolyte dynamics is developed. The model is then validated against the experimental data gathered from two batteries with different chemistries (lithium nickel manganese cobalt oxide/graphite and lithium iron phosphate oxide/graphite) at different input conditions. Results show that the reduced-order model agrees very well with experimental data at various conditions. Meanwhile, it can be simulated thousands of times faster than the real time, making it suitable for long-term-life simulation, control, and estimation applications.