Expanding Research on Second‑Life EV Batteries: AI‑Based Monitoring, Recycling Strategies, and Policy Innovations

New Energy Exploitation and Application

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Expanding Research on Second‑Life EV Batteries: AI‑Based Monitoring, Recycling Strategies, and Policy Innovations

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https://doi.org/10.54963/neea.v4i2.1353
Bazan, M. N., Subburaj, A. S., Partheepan, J., & Vinitha Hannah Subburaj. (2025). Expanding Research on Second‑Life EV Batteries: AI‑Based Monitoring, Recycling Strategies, and Policy Innovations. New Energy Exploitation and Application, 4(2), 157–184. https://doi.org/10.54963/neea.v4i2.1353
Volume 4 Issue 2 (2025): In Progress
Copyright (c) 2025 Marvin Norena Bazan, Anitha Sarah Subburaj, Joshua Partheepan, Vinitha Hannah Subburaj
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Authors

  • Marvin Norena Bazan

    College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
  • Anitha Sarah Subburaj

    College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
  • Joshua Partheepan

    College of Engineering, West Texas A&M University, Canyon, TX 79016, USA
  • Vinitha Hannah Subburaj

    College of Engineering, West Texas A&M University, Canyon, TX 79016, USA

Received: 1 July 2025; Revised: 8 September 2025; Accepted: 14 September 2025; Published: 10 October 2025

The rapid adoption of electric vehicles (EVs) has intensified concerns over the sustainable management of lithium‑ion batteries (LIBs), which often retain up to 80% of their capacity after reaching end‑of‑first‑life in vehicular applications. This study advances second‑life battery (SLB) research through three components: artificial intelligence (AI)‑driven predictive maintenance, optimized recycling strategies, and enabling policy frameworks. A synthesis of 51 peer‑reviewed sources published between 2019 and 2025, combined with case analyses from Tesla and Nissan pilots, supports the development of an integrated framework. Findings show that AI‑enhanced diagnostics can extend second‑life battery service by up to 50% and reduce lifecycle costs by 25%. Hybrid recycling processes can recover over 95% of critical materials—lithium, cobalt, and nickel—while lowering energy consumption by up to 20–40%. Policy incentives and adaptive regulations can reduce adoption barriers by 30–40%, facilitating large‑scale integration. The structured survey (n = 121) in this study revealed low public awareness, with 83% of respondents unaware of reuse potential. However, respondents expressed moderate willingness to adopt second‑life batteries, provided AI monitoring ensures at least 90% safety and performance reliability. By unifying technical, economic, and policy dimensions, this study demonstrates that AI‑enabled monitoring, advanced recycling, and adaptive regulation collectively support the alignment of second‑life EV batteries with global sustainability goals. The proposed framework underscores the importance of bridging innovation, market readiness, and governance to accelerate sustainable energy transitions.

Keywords:

Electric Vehicles Second‑Life Batteries Lithium‑Ion Battery Recycling AI-Based Predictive Maintenance Circular Economy Battery Disposal Sustainable Energy Storage Policy Frameworks

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