This paper presents an energy-aware multi-UAV coverage path planning algorithm that optimizes flight speed to minimize energy consumption. The method uses boustrophedon decomposition to divide the area of interest into sub-polygons and represents the problem as a Multiple Set Traveling Salesman Problem (MS-TSP) with energy constraints. The algorithm calculates the optimal flight speed for each UAV based on its physical parameters and uses a fast energy consumption estimation algorithm during the planning phase. The method outperforms state-of-the-art approaches 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 algorithm is open-sourced and includes a video and code repository. The paper also discusses related work, including existing methods for coverage path planning with UAVs, and compares the proposed method with other approaches in simulation and real-world experiments. The results show that the proposed method is more energy-efficient and faster in computation than existing methods, particularly in complex scenarios. The method is suitable for applications requiring energy-efficient and real-time coverage planning with multiple UAVs.This paper presents an energy-aware multi-UAV coverage path planning algorithm that optimizes flight speed to minimize energy consumption. The method uses boustrophedon decomposition to divide the area of interest into sub-polygons and represents the problem as a Multiple Set Traveling Salesman Problem (MS-TSP) with energy constraints. The algorithm calculates the optimal flight speed for each UAV based on its physical parameters and uses a fast energy consumption estimation algorithm during the planning phase. The method outperforms state-of-the-art approaches 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 algorithm is open-sourced and includes a video and code repository. The paper also discusses related work, including existing methods for coverage path planning with UAVs, and compares the proposed method with other approaches in simulation and real-world experiments. The results show that the proposed method is more energy-efficient and faster in computation than existing methods, particularly in complex scenarios. The method is suitable for applications requiring energy-efficient and real-time coverage planning with multiple UAVs.