From First Life to Second Life: Advances and Research Gaps in Prognosis Techniques for Lithium-Ion Batteries

Abstract

The growing use of lithium-ion batteries (LIBs) in electric vehicles has accelerated the need for efficient strategies to extend their lifespan through second-life applications, where retired batteries are repurposed for stationary storage and other less demanding roles. This paper reviews the most pertinent degradation mechanisms underlying battery aging and the most frequently occurring faults during battery operation. After establishing the correlation between degradation and fault occurrence, reliable state-of-health (SOH) and remaining useful life (RUL) predictions are identified as central to ensuring safety, reliability, and cost-effectiveness in repurposed systems. Next, we present a systematic review of the recently published studies on battery prognosis, with methods categorized into three groups: (i) physics-informed and hybrid models; (ii) purely data-driven approaches; and (iii) transfer learning and features extraction methods. A comparative analysis highlights the strengths and limitations of each group and identifies the most promising approaches for battery repurposing. Modeling heterogeneous second-life packs remains particularly challenging, as cells often enter repurposing with different usage histories and only partial BMS records. In this context, transfer learning and domain adaptation emerge as the most promising directions. In parallel, Generative Adversarial Networks (GANs) can help in addressing the challenge of data scarcity, particularly when integrated into hybrid frameworks for second-life applications. At the same time, systematic exploration of health indicators—including the possibility of stage-specific ones—remains essential. Finally, reinforcement learning offers a complementary yet still underexplored path, enabling real-time adaptation in dynamic scenarios, as batteries enter nonlinear regimes beyond the knee point.