The deployment of AI-enabled battery digital twins for battery management systems (BMS) and DC fast-charging control is often approached as a purely technical task focused on improving prediction accuracy and charging speed. This study argues that such deployments are inherently socio-technical, shaped by boundary judgments about system purpose, beneficiaries, measures of success, decision authority, resources, expertise, and legitimacy. Using Critical Systems Heuristics (CSH), we conducted semi-structured interviews with 22 experts and stakeholders spanning BMS/battery engineering, fast-charging operations, system integration, and safety/regulatory perspectives. Interview transcripts were analyzed using template analysis aligned with the four CSH dimensions (motivation, control, expertise, and legitimacy) to contrast “what is” versus “what ought to be” boundary judgments. Results show that current practice is predominantly throughput-driven, with battery longevity, thermal safety, and grid impacts treated as secondary constraints; decision authority is fragmented across vendors, OEM-side BMS logic, operators, and utilities; and resource allocation underinvests in calibration, data governance, cybersecurity, and continuous validation. The boundary critique was translated into a structured set of CSH-informed design requirements, emphasizing health-aware charging control, explicit safety envelopes and fail-safe modes, calibrated sensing and data-quality gates, drift detection and continuous validation, auditable decision logging, and multi-stakeholder governance and representation mechanisms. The study contributes a boundary-aware pathway for designing and governing trustworthy digital-twin-enabled fast charging beyond algorithmic performance.