Diagnosing and Decoupling the Degradation Mechanisms in Lithium Ion Cells: An Estimation Approach

TL;DR

This paper presents a novel estimation approach using an electrochemical model to diagnose and decouple different degradation mechanisms in lithium-ion batteries from EVs, without invasive testing.

Contribution

It introduces a new methodology to identify and quantify individual battery aging processes from real-world EV data, bypassing traditional tear-down methods.

Findings

Successful decoupling of degradation mechanisms

Correlation between model parameters and aging processes

Feasibility of non-invasive battery health diagnosis

Abstract

Understanding battery degradation in electric vehicles (EVs) under real-world conditions remains a critical yet under-explored area of research. Central to this investigation is the challenge of estimating the specific degradation modes in aged cells with no available information on usage history, bypassing the conventional yet invasive method of tear-down tests. Using an electrochemical model, this study pioneers a methodology to decouple and isolate the aging mechanisms in batteries sourced from EVs with varying mileages. A robust correlation is established between the model parameters and distinct degradation processes, enabling the diagnosis and estimation of each mechanism's impact on the battery's parameters. This paper sheds light on battery degradation in real-world scenarios and demonstrates the feasibility of their identification, isolation, and approximate quantification of their effects.