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Lithium-ion battery models for performance and aging

Time: Thu 2022-12-08 10.00

Location: F3, Lindstedtsvägen 26 & 28, Stockholm

Video link:

Language: English

Subject area: Chemical Engineering

Doctoral student: Jing Ying Ko , Tillämpad elektrokemi

Opponent: Professor Ulrike Krewer, Karlsruhe Institute of Technology

Supervisor: Professor Göran Lindbergh, Tillämpad elektrokemi

QC 2022-11-10


The demand for lithium-ion batteries (LiBs) is increasing at an exponential rate, so is the need to understand their behaviors and properties.In this pursuit, models are useful tools for improving cell design,quality, control system, keeping track of performance, aging andlifetime. The pseudo two-dimensional (P2D) model and its reduction,single particle model (SPM), are the cornerstone of LiBs’ physics-basedmodel. They entail properties that relate directly to battery’s behaviors.The P2D model and SPM are generally applicable, but lack themathematical description of specific physical phenomena. As such,model extensions are required to capture additional phenomena.

This work outlines the modeling approach for specific processes like mechanical stress, capacitance and current distribution, and aging. Slow processes like solid lithium diffusion and particle mechanical stress are studied for pulse polarization and relaxation. Fast processes like charge transfer and double layer charging play an important role in addressing the behavior of depressed-shaped semicircle arcs in impedance Nyquist plot.

Aging under two types of cycling conditions is investigated, namely high voltage and partial cycling. The conditions target the different performance requirements for LiBs in electric vehicle and stationary energy storage system markets. Electrode properties and aging parameters are extracted. A dynamic lifetime model studies solid electrolyte interface and particle cracking in commercial cells, then predicts the remaining useful life.

Physics-based models and their extended versions contain many parameters.It is unlikely that all parameters are known. Therefore,parametrization is implemented to extract unknown values. In this thesis, parametrization combines electrochemical data and extended models. Electrode’s physical, aging and lifetime parameters are extracted from experimental characterization tests.