Electrode-Level State Estimation in Lithium-Ion Batteries via Kalman Decomposition

Abstract

Lithium-ion battery electrode-level online state estimation using high-fidelity nonlinear electrochemical models remains a key challenge. This is particularly due to weak observability inherited from the complex model structure, even for reduced-order electrochemical models. This manuscript presents a systematic and rigorous strategy to analyze the local observability of a single particle model (SPM) with both electrodes, which is commonly known to be locally unobservable from current-voltage measurements. Estimating the essential states, e.g. state of charge (SOC) and solid-phase lithium surface concentration, is crucial for battery charge and health monitoring since different degradation mechanisms affect each electrode individually. In this manuscript, the proposed observability analysis approach based on the Kalman decomposition enables provably convergent estimates. Ultimately, using the observability analysis, we propose a state estimator based on the nonlinear SPM dynamics and prove estimation error system stability. The observability analysis and state estimation scheme exploits the conservation of lithium property. Simulations demonstrate the effectiveness of the electrode-level state estimator as opposed to the cell-level estimator.

PDE Observer for All-Solid-State Batteries via an Electrochemical Model

“…= eq ( ( )) − eq ( ( )), (13) in which eq denotes the equilibrium potential for the cathode, whereas ( ) = ( , ) and ( ) is the normalized volume-averaged (bulk) Li concentration in the cathode. Empirical functions of eq can be obtained via experimental curve fitting [16], and thermodynamically consistent opencircuit curves may be found through the Redlish-Kister formalism [21].…”

“…where the respective terms were presented in ( 5), (11), and (13). Ultimately, the output voltage is the combined effect from equilibrium voltage and total overpotential,…”

“…The majority of research in battery modeling and control has been centered around conventional Li-ion batteries with porous composite electrodes [10], [11], [12], [13], and very few efforts have been dedicated to solid-state batteries. Danilov et al developed a one-dimensional model to simulate the performance of a Li|Li 3 PO 4 |LiCoO 2 cell [14], accounting for charge transfer kinetics, lithium diffusion in electrode, and diffusion and migration of ions in solid electrolyte.…”

Thermal-Enhanced Adaptive Interval Estimation in Battery Packs With Heterogeneous Cells

“…To date, different battery models oriented towards state and parameter estimator designs for single battery cell have been extensively proposed in the literature, which can be classified into electrochemical white box (first principle) models [4], [5], [6], [7], [8], equivalent-circuit gray box models [9], [10], [11], [12] and data-driven black box models [13], [14], [15]. Electrochemical models describe the diffusion, thermodynamics, and electrochemical kinetics.…”

Battery Electrode Mass Loading Prognostics and Analysis for Lithium-Ion Battery–Based Energy Storage Systems

“…With the rapid development of artificial intelligence and machine learning technology, data-driven strategies have been widely used as an efficient tool in the field of battery management (Liu et al, 2019a;. Numerous research studies have been carried out to design suitable data-driven solutions to benefit battery internal-state estimation (Feng et al, 2020;Zhang et al, 2020), lifetime, or future aging prognostics in both cycling (Lucu et al, 2020;Tang et al, 2020) and calendar modes (Liu et al, 2019b), fault diagnostics (Yang et al, 2018;Wang et al, 2021), cell equalization (Ouyang et al, 2019;Liu et al, 2020;Song et al, 2020), charging control (Ban et al, 2021;Wei et al, 2021), thermal management (Xie et al, 2021a;Xie et al, 2021b), and energy management (Liu et al, 2019c;Wu et al, 2020;Chen et al, 2021). Overall, after deriving these data-driven solutions, a more efficient and smarter battery management can be achieved.…”