Pulse Voltammetry Analysis of Li-Ion Battery Degradation

Abstract

Pulse voltammetry is a class of electroanalytical techniques which involve applying voltage pulses to understand the redox reaction in an electrochemical system. The advantages of such a technique is high sensitivity of pulse voltammograms towards kinetic and thermodynamic parameters. Here, a physics-based model is developed to simulate the Li-ion battery physics and the resultant pulse voltammogram for generic voltage pulses. We develop an in-situ diagnostic tool by combining it with a parameter estimation technique to accurately, rapidly and quantitatively capture the key parameters. A combined experimental and computational-modelling approach allows us to parametrize the state of Li-ion battery system. Degradation reactions such as solid electrolyte interphase growth, active material slippage, and loss have been considered to capture the pertinent degradation modes responsible for capacity fade under different experimental conditions. This method can be extended to integrate with battery management systems for state of health estimation and monitoring.The objective of this effort is to quantitatively characterize battery properties in order to develop accurate physics-based models suitable for model-based development of fast charging protocols and accurate state-of-charge and state-of-health prediction. Pulse voltammetry analysis software and battery electrode characterization approaches provide the ability to determine quantitative values, with known confidence intervals, for input parameters of physics-based models describing battery performance. Pulse voltammetry characterization, in conjunction with physical analysis and complementary techniques such as galvanostatic intermittent titration (GITT) and electrochemical impedance spectroscopy (EIS) will be utilized to analyze the battery state. These quantitative methods enable the development and parameterization of predictive models for both battery performance and undesired processes, such as lithium plating, that limit charging rates and cycle life of lithium-ion batteries.This presentation will focus on DPV data gathered periodically during cell cycling to identify the progression of capacity loss and degradation. The DPV analysis approach, which utilizes the experimental data to estimate kinetic and thermodynamic parameters of the model for battery performance, will be presented. The experimental characterization results will include example DPV measurements as a function of state-of-charge, cycling history, and operational temperature. Examples of data analysis to estimate model parameters with statistical confidence measures, and interpretation of the change in voltammogram response with cycling will be discussed. Figure 1. Fitting model results with experimental differential pulse voltammetry data for parameterization Figure 1