  Battery aging is a natural process that contributes to capacity and power
fade, resulting in a gradual performance degradation over time and usage. State
of Charge (SOC) and State of Health (SOH) monitoring of an aging battery poses
a challenging task to the Battery Management System (BMS) due to the lack of
direct measurements. Estimation algorithms based on an electrochemical model
that take into account the impact of aging on physical battery parameters can
provide accurate information on lithium concentration and cell capacity over a
battery's usable lifespan. A temperature-dependent electrochemical model, the
Enhanced Single Particle Model (ESPM), forms the basis for the synthesis of an
adaptive interconnected observer that exploits the relationship between
capacity and power fade, due to the growth of Solid Electrolyte Interphase
layer (SEI), to enable combined estimation of states (lithium concentration in
both electrodes and cell capacity) and aging-sensitive transport parameters
(anode diffusion coefficient and SEI layer ionic conductivity). The practical
stability conditions for the adaptive observer are derived using Lyapunov's
theory. Validation results against experimental data show a bounded capacity
estimation error within 2% of its true value. Further, effectiveness of
capacity estimation is tested for two cells at different stages of aging.
Robustness of capacity estimates under measurement noise and sensor bias are
studied.
