Characterization of the degradation process of lithium-ion batteries when discharged at different current rates

Abstract

The use of energy storage devices, such as lithium-ion (Li-ion) batteries, has become popular in many different domains and applications. Hence, it is relatively easy to find literature associated with problems of battery state-of-charge estimation and energy autonomy prognostics. Despite this fact, the characterization of battery degradation processes is still a matter of ongoing research. Indeed, most battery degradation models solely consider operation under nominal (or strictly controlled) conditions, although actual operating profiles (including discharge current) may differ significantly from those. In this context, this article proposes a Li-ion battery degradation model that incorporates the impact of arbitrary discharge currents. Also, the proposed model, initially calibrated through data reported for a specific Li-ion battery type, can characterize degradation curves for other Li-ion batteries. Two case studies have been carried out to validate the proposed model, initially calibrated by using data from a Sony battery. The first case study uses our own experimental data obtained for a Panasonic Li-ion cell, which was cycled and degraded at high current rates. The second case study considers the analysis of two public data sets available at the Prognostics Center of Excellence of NASA Ames Research Center website, for batteries cycled using nominal and 2-C (twice the nominal) discharge currents. Results show that the proposed model can characterize degradation processes properly, even when cycles are subject to different discharge currents and for batteries not manufactured by Sony (whose data was used for the initial calibration).

Data-Driven Diagnostics and Prognostics for Modelling the State of Health of Maritime Battery Systems – a Review

“…Analytical models proposed in the literature include numerous forms of relationships between capacity fade and number of cycles/time, such as linear (Belt, Utgikar, & Bloom, 2011), square-root (F. Yang, Song, Dong, & Tsui, 2019), power-law (Schmalstieg, Käbitz, Ecker, & Sauer, 2014;Han, Ouyang, Lu, & Li, 2014), exponential (X. Zhang, Miao, & Liu, 2017;Tang et al, 2019;Perez et al, 2018), polynomial (Micea, Ungurean, Cârstoiu, & Groza, 2011), sigmoid (Johnen et al, 2020) or a combination of these (Xing, Ma, Tsui, & Pecht, 2013). More complicated models also account for differences in C-rates and temperatures (Ji et al, 2020;Singh, Chen, Tan, & Huang, 2019).…”

A rapid neural network–based state of health estimation scheme for screening of end of life electric vehicle batteries

“…The problem of state measurement of LIBs has been widely studied, most notably for estimation of the battery state of charge (SoC), while literature pertaining to SoH estimation remains less prevalent. Focuses of recent works in the area of SoH estimation have included incremental capacity (IC)/differential voltage (DV) measurement, 19 Coulomb counting, 20 (dual) extended Kalman filters 21,22 or empirical health degradation models such as those developed by Perez et al 23 However, the bulk of literature pertaining to SoH estimation focuses on prognostics and health management of existing battery systems, 24 with less emphasis placed on end of life characterisation of batteries. Furthermore, a majority of SoH estimation works focus on single cells.…”

Reliable state of health condition monitoring of Li-ion batteries based on incremental support vector regression with parameters optimization

“…The test bed is designed for accelerated battery degradation, and it contains several experimental indicators such as the power supply rate, battery voltage, battery amperage, battery temperature and also environmental temperature, and so on. 8 The test bed contains a single-board computer in order to control the experiment conditions. 9…”

“…Currently, most researches are focused on datadriven prognostics based on the sensor historical testing data, and applying this to battery RUL estimation. [6][7][8][9] In Cheng et al, 10 the Gauss-Hermite particle filter was used to define a new state to estimate accurately the capacity. Then, based on the estimates values, a numerical projection with a long horizon was used to project the capacity fade to the value of the total degradation and consequently to outline the corresponding time.…”