This paper presents an energy-aware multi-UAV coverage path planning (mCPP) method that optimizes energy consumption for UAVs while ensuring coverage of a given area of interest (AOI). The method uses optimal flight speed to minimize energy consumption per traveled distance and a precise energy consumption estimation algorithm during the planning phase. The problem is decomposed using boustrophedon decomposition and represented as an instance of the Multiple Set Traveling Salesman Problem (MS-TSP) with energy constraints. The proposed method outperforms state-of-the-art methods in terms of computational time and energy efficiency. Experimental results show that the energy consumption estimation has an average accuracy of 97% compared to real flight consumption. The method was verified in a real-world experiment with two UAVs. The key contributions include proposing energy consumption as an objective in mCPP, reducing the CPP problem to MS-TSP formulation, and directly considering battery capacity constraints during planning. The method is open-sourced and has been tested in simulation and real-world scenarios, demonstrating its effectiveness in energy-efficient multi-UAV coverage missions. The algorithm uses a combination of trajectory generation and fast energy estimation to minimize energy consumption while accounting for battery limitations. The method is efficient, accurate, and suitable for real-world deployment. The results show that the proposed method outperforms existing approaches in terms of energy efficiency and computational time, making it a promising solution for energy-aware multi-UAV coverage missions.This paper presents an energy-aware multi-UAV coverage path planning (mCPP) method that optimizes energy consumption for UAVs while ensuring coverage of a given area of interest (AOI). The method uses optimal flight speed to minimize energy consumption per traveled distance and a precise energy consumption estimation algorithm during the planning phase. The problem is decomposed using boustrophedon decomposition and represented as an instance of the Multiple Set Traveling Salesman Problem (MS-TSP) with energy constraints. The proposed method outperforms state-of-the-art methods in terms of computational time and energy efficiency. Experimental results show that the energy consumption estimation has an average accuracy of 97% compared to real flight consumption. The method was verified in a real-world experiment with two UAVs. The key contributions include proposing energy consumption as an objective in mCPP, reducing the CPP problem to MS-TSP formulation, and directly considering battery capacity constraints during planning. The method is open-sourced and has been tested in simulation and real-world scenarios, demonstrating its effectiveness in energy-efficient multi-UAV coverage missions. The algorithm uses a combination of trajectory generation and fast energy estimation to minimize energy consumption while accounting for battery limitations. The method is efficient, accurate, and suitable for real-world deployment. The results show that the proposed method outperforms existing approaches in terms of energy efficiency and computational time, making it a promising solution for energy-aware multi-UAV coverage missions.