Mathematical Modeling of Battery Degradation Based on Direct Measurements and Signal Processing Methods

• Published in: Applied sciences Vol. 13; no. 8; p. 4938
• Main Authors: de la Vega, Joaquín, Riba, Jordi-Roger, Ortega-Redondo, Juan Antonio
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-04-2023
• Subjects: Accuracy; Aging; Algorithms; Batteries; battery; capacity; Data points; degradation; Electrolytes; Energy storage; health indicator; Lithium; Lithium-ion batteries; Mathematical models; Methods; Neural networks; prediction; Signal processing; state of charge
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13084938

This paper proposes and evaluates the behavior of a new health indicator to estimate the capacity fade of lithium-ion batteries and their state of health (SOH). This health indicator is advantageous because it does not require the acquisition of data from full charge–discharge cycles, since it is calculated within a narrow SOC interval where the voltage vs. SOC relationship is very linear and that is within the usual transit range for most practical charge and discharge cycles. As a result, only a small fraction of the data points of a full charge–discharge cycle are required, reducing storage and computational resources while providing accurate results. Finally, by using the battery model defined by the Nernst equation, the behavior of future charge–discharge cycles can be accurately predicted, as shown by the results presented in this paper. The proposed approach requires the application of appropriate signal processing techniques, from discrete wavelet filtering to prediction methods based on linear fitting and autoregressive integrated moving average algorithms.