From Motion to Energy: Health-Aware Motion Planning for Enhanced Predictive Energy Management in Hybrid Fuel Cell/Battery Autonomous Mobile Robots

Autonomous mobile robots (AMRs) operating in industrial and urban environments must function efficiently under dynamic and energy-constrained conditions. Conventionally, motion planning and energy management are designed separately: planners generate feasible trajectories, while the energy management system (EMS) reactively supplies power without anticipating future demand. In hybrid robots powered by batteries and fuel cells (FCs), this separation reduces long-term efficiency and accelerates component degradation. This work introduces a health-aware planning and predictive EMS architecture in which the motion planner explicitly incorporates battery state-of-charge (SoC) and FC state-of-health (SoH) predictions into trajectory optimization, while the EMS performs anticipative power allocation. By shaping motion to avoid stress-inducing load patterns, the framework promotes stable operation and durability. Long-duration evaluations across warehouse, urban delivery, and industrial forklift scenarios demonstrate reduced hydrogen consumption, smoother FC operation, and controlled battery cycling.