Online Aging Diagnostics Using Optimally Designed Experiments

Abstract

Degradation of lithium-ion batteries is the result of many complex phenomena occurring simultaneously at varying time and length scales. The underlying electrochemical and mechanical phenomena have received much attention from researchers [1]. Physics-based models of these effects support the mechanistic understanding of degradation modes and can thereby help reduce their severity. Few studies target changing electrochemical parameters such as diffusion coefficients or reaction rate constants that have a direct impact on model accuracy and manifest themselves in observable aging. Lyu et al. [2] used a simplified electrochemical model and monitored battery degradation by following changes in diffusion time constants, electrode balancing, reaction rate coefficients, and ohmic resistance. However, several of the parameters they attempted to track could only be identified with low accuracy as they used the same data-set to identify all parameters. In this study, we investigate parameters of a full order Newman-type model [3] over the course of a batteries lifetime under real-world load-cycles. To ensure parameter identifiability, optimally designed experiments are used for parameter estimation.In a previous study [4] the feasibility of optimal experiment design for parametrization of electrochemical battery models was demonstrated. We now extend this work and re-evaluate key parameters over the course of an aging study on commercial, nickel-rich 18650 lithium-ion batteries. We highlight how quantifying changes in physical battery parameters can extend standard performance metrics for a batteries state-of-health by, e.g., including degradation in rate-capability. Additionally, the importance of battery usage conditions such as C-rate or state-of-charge window on model parameter trajectories is investigated and their relationship with conventional performance metrics such as the bulk cell resistance or rate-capability determined.Quantifying how specific mechanisms contribute to apparent capacity or power fade is a major step towards battery lifetime optimization. This could enable designs more tailored for specific applications and significantly extend batteries useful lifetime. Furthermore, updating parameters is essential for electrochemical control strategies relying on accurate model predictions of battery states as illustrated in Figure 1. This re-calibration would make a battery management system aging-sensitive and enable more efficient utilization and a physics-informed state-of-health.Figure 1: The central plot shows how parameters change during aging. If this change is not considered, model performance deteriorates between beginning-of-life (BOL) and end-of-life (EOL) (in blue, right-hand side). This is normally handled by using conservative battery management systems and over-sizing systems. The proposed strategy (orange) achieves higher model accuracy during the entire useful life and the parameter estimates can be used to formulate an extended state-of-health.