Synergetic optimization of multi-layer energy networks for Lithium-ion battery manufacturing coupled production rhythms

To address the strong coupling between production processes and multi-layer energy networks (MENs), the mutual constraints between production rhythms and intermediate inventories, and the interactions between hourly electricity prices and renewable energy sources in lithium-ion battery factories, this paper proposes a coordinated production scheduling and energy management framework. An integrated multi-layer resource task network (MRTN) coupled with production rhythms and an MEN optimization model is developed. Specifically, an MRTN model is established to describe the sequential manufacturing stages of cell production, battery assembly, formation and aging, module assembly, and pack assembly, while explicitly considering production rhythms, equipment start stop logic, interstage buffer capacities, process time delays, and stage specific task completion volumes. An MEN model incorporating compressed air systems, ice storage, and heat recovery is then formulated, enabling the unified optimization of production and energy subsystems within a single framework. To accommodate real time disturbances that cannot be effectively handled by offline full-horizon optimization, a short term receding horizon model predictive control (MPC) strategy is further developed. Simulation results demonstrate that the proposed approach effectively coordinates production scheduling and multi-energy management while satisfying production rhythm and process constraints, reducing the total operating cost by 8.89% compared with the day-ahead planning strategy. • A multi-layer resource-task network integrates production and equipment constraints. • A multi-layer energy network coordinates air, ice, and heat recovery. • Rolling-horizon MPC cuts operating costs by 8.89% versus day-ahead planning.