Suggested Citation:
Shi, Dapai, Misheng Cai, Yunhong Che, Lili Xie, Jingyuan Zhao (2025)

Towards Artificial Intelligence-Enabled Autonomous Battery Prognostics and Management

Reliable and safe operation of batteries is increasingly challenged by diverse operating conditions and stringent demands for system resilience. Artificial intelligence (AI) has emerged as a transformative enabler of battery health management, offering capabilities beyond traditional models. This review provides a structured synthesis of recent progress in AI-enabled diagnostics. Advances in state estimation—including state of health (SOH) and remaining useful life (RUL)—are first examined, with methodological breakthroughs identified across diverse task formulations. The evolution of AI architectures is then traced, from conventional neural networks to attention-based Transformers, physics-informed models, and federated learning, with particular attention to emerging paradigms such as foundation models, neuro-symbolic reasoning, and quantum machine learning that promise improved robustness and interpretability. To bridge laboratory innovation with deployment, a domain-adaptive four-stage data pipeline has emerged as a promising framework for real-world BMS signals—spanning operational segmentation, multi-scale denoising, degradation-aware feature engineering, and structured sample construction—designed to enhance generalization under heterogeneous and noisy conditions. Looking forward, a technological roadmap is outlined that integrates edge AI, digital twins, AIOps, quantum computing, wireless sensing, and self-repair systems. Collectively, these innovations transform batteries from passive energy reservoirs into intelligent cyber-physical agents endowed with perception, autonomous decision-making, and resilient fault response—paving the way toward truly battery-centric autonomous energy systems.