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Identification of Li-ion batteries’ stress factors during vehicle’s service lifetime: Assessment and Analysis


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Accelerated aging of Li-ion batteries plays a crucial role during pre-marketing characterization for warranty and lifetime estimation. The current methodology applied, both in standards and in literature, is solely based on overstress testing (ISO 12405 2018, IEC 62660-1). Such approach, being de-correlated from usage data [1–3], is failing to estimate the degradation level of the battery for long periods of time [4], [5], as it engenders degradation mechanisms that do not occur in real-time usage. The general methodology for durability study proposed by Nelson and Carlson [6], [7] requires an in-depth understanding of field’s data vis-à-vis the aging of the cell, prior to elaborating an accelerated aging protocol. It is then crucial to determine the most frequent and influential stress factors encountered by the battery in real service lifetime. Even though EVs have been deployed for around a decade in the market and a relatively good amount of data have been studied, ranging from raw data to surveys, an exhaustive assessment is missing, eclipsing the way for accelerated aging researchers. This paper aims to bring together and analyze the available real world fleet data for all three kinds of EV applications (BEV, 48V-HEV and PHEV). The overview will focus on the operating temperature of the cells during driving conditions, the most occurring SOC values, as well as the cell’s power consumption. Results are taken from European, North American and far eastern projects, in order to determine the differences and similarities between stress factors in the different continents. This analysis is accompanied by additional raw data from a monitored BEV equipped with a direct refrigerant thermal management system from CEA-Grenoble, France.
[1] J. Schoch, “Battery Life Optimal Operation of Electric Vehicles,” Karlsruher Institut für Technologie (KIT), 2018.
[2] Baure and M. Dubarry, “Synthetic vs. Real Driving Cycles: A Comparison of Electric Vehicle Battery Degradation,” Batteries, vol. 5, p. 42, 2019.
[3] V. Ruiz, “Standards for the performance and durability assessment of electric vehicle batteries,” European Comission, 2018.
[4] J. de Hoog, J.-M. Timmermans, D. Ioan-Stroe, M. Swierczynski, J. Jaguemont, S. Goutam, N. Omar, J. [Van Mierlo], and P. [Van D. Bossche], “Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation,” Applied Energy, vol. 200, pp. 47–61, 2017.
[5] M. D. Gennaro, E. Paffumi, G. Martini, A. Giallonardo, S. Pedroso, and A. Loiselle-Lapointe, “A case study to predict the capacity fade of the battery of electrified vehicles in real-world use conditions,” Case Studies on Transport Policy, vol. 8, no. 2, pp. 517–534, 2020.
[6] W. Nelson, “Chapter 1: Introduction and Backgroud,” in Accelerated Testing: Statistical Models, Test Plans, and Data Analysis, John Wiley & Sons, 1990, pp. 1–50.
[7] B. Carlsson, “Chapter 4.1 – General Methodology,” in Performance and Durability Assessment, M. Köhl, B. Carlsson, G. Jorgensen, and A. W. Czanderna, Eds. Amsterdam: Elsevier, 2004, pp. 141–145.

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