A novel aging modeling approach for second-life lithium-ion batteries
Year: 2025
Type: article
Abstract: The electric mobility industry is booming. In order to reduce the environmental impact of this boom, there is the potential to reuse the batteries from electric vehicles. However, the technical and economic feasibility of the second-life of lithium-ion batteries remains in question. This is due to the intricate non-linear mechanisms that occur during battery degradation, leading to capacity and power loss. Ongoing research aims to create models that can predict the state of battery degradation. However, most studies have focused on the battery’s first life, operating within a limited state of health range and requiring constant monitoring of the battery’s exposure conditions. While these models provide satisfactory results for the battery’s performance in vehicles, they cannot be directly applied to second-life scenarios. In response to this issue, this article proposes a degradation modeling method for second-life batteries based on identifying and linearizing different degradation trends within the battery. This approach allows the application of the model without prior knowledge of the battery’s history. It has been validated for a state of health range of 95% to 20%, through both conventional charge-discharge tests and a real-world scenario involving a smart charging station for urban buses. The results obtained with the developed model are overall satisfactory, achieving a MAPE below 3% for capacity and 1.4% for internal resistance in the real-world scenario.
Source: eTransportation
Authors: Ane Pérez, Idoia San Martín, Pablo Sanchis, Alfredo Ursúa
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,580
Awards: G1175115578, G1288135697, G174780211, G1961612201, G2262748287, G24120701, G2550080368, G2601397997, G294895050, G300979063, G3222506250, G3480869486, G3508427142, G3681454997, G4042783231, G4047719527, G410571670, G425668148, G451917667, G5080475149, G5195188938, G523076202, G5338730000, G5539308778, G5967599077, G6033112984, G7084143925
OpenAlex ID: W4407319033