<p>Flexible Job Shop Scheduling Problems (FJSP) traditionally assume infinite transportation resources or simplified transportation constraints. With the rise of intelligent manufacturing, Automated Guided Vehicles (AGVs) have emerged as essential transport resources due to their high flexibility and autonomy. The limited availability of AGVs can significantly impact overall production efficiency. Meanwhile, growing concerns about energy consumption and environmental sustainability underscore the necessity of incorporating energy-related objectives into scheduling decisions. In this context, this paper addresses the Energy-aware FJSP with limited AGVs (EFJSP-AGV). A multi-objective mixed-integer programming (MMIP) model is developed to simultaneously minimize makespan and total energy consumption (TEC). To efficiently tackle this challenging problem, a knowledge-driven memetic algorithm (KDMA) is proposed. Specifically, an integrated initialization approach is devised to efficiently generate promising initial solutions. Furthermore, a knowledge-driven variable neighborhood search (VNS) tailored to the characteristics of the problem is developed to enhance the exploitation within the solution space. Additionally, effective energy-aware strategies for reducing energy consumption are incorporated to achieve lower total energy usage. Experimental results indicate that the proposed KDMA outperforms comparison algorithms, validating its effectiveness in solving the EFJSP-AGV.</p>