On-line Capacity Estimation for Lithium-ion Battery Cells via an Electrochemical Model-based Adaptive Interconnected Observer

TL;DR

This paper introduces an adaptive observer based on an electrochemical model to accurately estimate lithium-ion battery capacity and aging-related parameters, improving BMS capabilities over the battery's lifespan.

Contribution

It develops a temperature-dependent electrochemical model and an adaptive interconnected observer for simultaneous estimation of battery states and aging parameters, validated with experimental data.

Findings

Capacity estimation error within 2%

Effective estimation across different aging stages

Robustness under measurement noise

Abstract

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.