AI for EV battery health: Statistical meta-analysis and Autonomous Intelligence Pyramid

Abstract

The transportation sector accounts for approximately 21% of global CO 2 emissions ( ≈ 8.4 Gt CO 2 in 2024), and the electrification of road transport is a critical lever for decarbonisation. Annual global EV battery deployment reached 1.2 TWh in 2025, making accurate battery health monitoring essential for both performance and sustainability. This review, conducted via a PRISMA-based protocol over 531 peer-reviewed studies (2018–2026), provides a statistical meta-analysis of all major AI families deployed in battery management: classical machine learning, ensemble methods, deep sequential networks, hybrid CNN architectures, Transformers, Physics-Informed ML (PIML), Explainable AI (XAI), Federated Learning, and Large Battery Foundation Models. A DerSimonian–Laird random-effects meta-analysis with 95% confidence intervals and I 2 heterogeneity statistics confirms that CNN–LSTM achieves a pooled RMSE of 0.47% (95% CI: 0.41%–0.53%, d = 3.42 vs EKF baseline) and Transformer architectures reach 0.42% (0.36%–0.48%, d = 3.78 ). The Foundation Model group, based on only nine studies at TRL 2–3, yields a preliminary pooled RMSE of 0.42% that is sensitivity-dependent on a single influential study; this figure should not be directly compared with the more robustly supported Transformer estimate. Critical limitations of all emerging methods are explicitly characterised. Environmental and social impacts of battery energy systems, including CO 2 lifecycle analysis, are discussed. An author-proposed five-layer Autonomous Battery Intelligence Pyramid is presented as a speculative research roadmap, with a dedicated implementation pathway discussion, clearly distinguished throughout from experimentally validated findings.