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Article

A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications

by
Marran Al Qwaid
1,
Gobbi Ramasamy
2,* and
Md Sabbir Hossen
2
1
Department of Computer Sciences, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia
2
Faculty of AI and Engineering, Multimedia University, Cyberjaya 63100, Malaysia
*
Author to whom correspondence should be addressed.
Energies 2026, 19(16), 3876; https://doi.org/10.3390/en19163876
Submission received: 28 July 2026 / Revised: 13 August 2026 / Accepted: 16 August 2026 / Published: 18 August 2026
(This article belongs to the Topic Electric Vehicles Energy Management, 2nd Volume)

Abstract

The increasing adoption of electric vehicles (EVs) is expected to generate a substantial number of retired lithium-ion batteries, creating both environmental challenges and opportunities for second-life energy storage applications. However, the performance variability of retired batteries makes the identification of suitable candidates for repurposing a significant challenge. This study proposes a Battery Stability Index (BSI) framework for evaluating the suitability of second-life EV batteries for stationary energy storage applications supporting EV charging infrastructures. Battery cycling data from Nissan Leaf and Mitsubishi i-MiEV battery packs were analyzed using degradation rate, energy efficiency retention, operational stability, and energy throughput indicators. In addition, EV charging demand data were utilized to assess charging session support capability as a practical deployment-oriented performance metric. The proposed BSI integrates stability, degradation, and throughput characteristics into a unified assessment framework for battery ranking and suitability evaluation. The results demonstrate significant performance differences between the evaluated battery families. Nissan Leaf batteries exhibited lower capacity degradation rates (0.0086) than Mitsubishi i-MiEV batteries (0.0170), maintained higher energy efficiency retention (91.4% versus 79.0%), and achieved substantially greater energy throughput (21,911 Wh versus 6230 Wh). Furthermore, Nissan Leaf batteries supported up to 1.16 EV charging sessions, whereas Mitsubishi i-MiEV batteries supported fewer than 0.34 sessions. Consequently, Nissan Leaf batteries achieved the highest BSI values, with Leaf-1 and Leaf-2 obtaining scores of 0.658 and 0.638, respectively. The findings demonstrate that battery suitability cannot be reliably determined using a single health indicator. The proposed BSI framework provides a comprehensive and practical approach for identifying suitable second-life batteries, supporting battery repurposing decisions, sustainable energy storage deployment, and the integration of second-life batteries within EV charging ecosystems.
Keywords: second-life electric vehicle batteries; battery suitability assessment; Battery Stability Index; battery degradation; stationary energy storage; EV charging stations; energy throughput; circular economy second-life electric vehicle batteries; battery suitability assessment; Battery Stability Index; battery degradation; stationary energy storage; EV charging stations; energy throughput; circular economy

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MDPI and ACS Style

Al Qwaid, M.; Ramasamy, G.; Hossen, M.S. A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications. Energies 2026, 19, 3876. https://doi.org/10.3390/en19163876

AMA Style

Al Qwaid M, Ramasamy G, Hossen MS. A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications. Energies. 2026; 19(16):3876. https://doi.org/10.3390/en19163876

Chicago/Turabian Style

Al Qwaid, Marran, Gobbi Ramasamy, and Md Sabbir Hossen. 2026. "A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications" Energies 19, no. 16: 3876. https://doi.org/10.3390/en19163876

APA Style

Al Qwaid, M., Ramasamy, G., & Hossen, M. S. (2026). A Data-Driven Framework for Assessing Second-Life Electric Vehicle Batteries for Stationary Energy Storage Applications. Energies, 19(16), 3876. https://doi.org/10.3390/en19163876

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