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.
Physics-guided residual Kalman learning for state-of-charge estimation of lithium iron phosphate batteries
VITO（维托研究所）；EnergyVille（能源村）；Institute for Materials Research (IUMAT), Hasselt University（哈瑟尔特大学材料研究所）；College of Tianyin Lake Science and Technology Innovation, Nanjing Institute of Technology（南京工程学院天目湖学院）；School of Vehicles and Intelligent Transportation, Fuyao University of Science and Technology（福耀科技大学车辆与智能交通学院）；IUMAT, IMEC（IMEC-哈瑟尔特大学材料研究所）
