Intelligent Battery Systems: System‐Level Integration of Data‐Driven Learning, Optimisation and Predictive Management

ABSTRACT The rapid expansion of electrified energy systems has transformed batteries from passive storage components into critical infrastructure assets. This shift has intensified persistent challenges related to degradation, safety and lifecycle uncertainty. A growing body of literature examines the use of artificial intelligence (AI) and machine learning (ML) in battery modelling and management. However, most extant studies emphasise individual algorithms or isolated prediction tasks. The reliable deployment of intelligence on operational battery system receives limited attention. This perspective addresses this limitation by examining intelligent battery systems from a system‐level standpoint. Intelligence is approached as an outcome of coordinated integration across sensing, learning, optimisation and management functions, rather than as an isolated algorithmic feature. Recent advances in data acquisition, performance optimisation, lifecycle modelling and predictive management are examined within a unified battery management architecture. Attention is given to practical deployment constraints, including sensing fidelity, data representativeness, interpretability, scalability and compatibility with embedded control systems. Evidence from fast charging studies and grid‐scale deployments highlights both the potential of AI‐driven approaches and the structural barriers that restrict translation from laboratory settings to real‐world operation. The analysis highlights the role of early and diverse sensing, physics‐informed learning, uncertainty‐aware decision processes and deployment‐oriented model design in shaping dependable system‐level intelligence. The paper concludes by identifying research priorities that align algorithmic development with safety, sustainability and operational feasibility, positioning intelligent battery systems as adaptive assets within future energy infrastructures.